17,844 research outputs found

    A hybrid quantum algorithm to detect conical intersections

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    Conical intersections are topologically protected crossings between the potential energy surfaces of a molecular Hamiltonian, known to play an important role in chemical processes such as photoisomerization and non-radiative relaxation. They are characterized by a non-zero Berry phase, which is a topological invariant defined on a closed path in atomic coordinate space, taking the value π\pi when the path encircles the intersection manifold. In this work, we show that for real molecular Hamiltonians, the Berry phase can be obtained by tracing a local optimum of a variational ansatz along the chosen path and estimating the overlap between the initial and final state with a control-free Hadamard test. Moreover, by discretizing the path into NN points, we can use NN single Newton-Raphson steps to update our state non-variationally. Finally, since the Berry phase can only take two discrete values (0 or π\pi), our procedure succeeds even for a cumulative error bounded by a constant; this allows us to bound the total sampling cost and to readily verify the success of the procedure. We demonstrate numerically the application of our algorithm on small toy models of the formaldimine molecule (\ce{H2C=NH}).Comment: 15 + 10 pages, 4 figure

    Implementing Health Impact Assessment as a Required Component of Government Policymaking: A Multi-Level Exploration of the Determinants of Healthy Public Policy

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    It is widely understood that the public policies of ‘non-health’ government sectors have greater impacts on population health than those of the traditional healthcare realm. Health Impact Assessment (HIA) is a decision support tool that identifies and promotes the health benefits of policies while also mitigating their unintended negative consequences. Despite numerous calls to do so, the Ontario government has yet to implement HIA as a required component of policy development. This dissertation therefore sought to identify the contexts and factors that may both enable and impede HIA use at the sub-national (i.e., provincial, territorial, or state) government level. The three integrated articles of this dissertation provide insights into specific aspects of the policy process as they relate to HIA. Chapter one details a case study of purposive information-seeking among public servants within Ontario’s Ministry of Education (MOE). Situated within Ontario’s Ministry of Health (MOH), chapter two presents a case study of policy collaboration between health and ‘non-health’ ministries. Finally, chapter three details a framework analysis of the political factors supporting health impact tool use in two sub-national jurisdictions – namely, Québec and South Australia. MOE respondents (N=9) identified four components of policymaking ‘due diligence’, including evidence retrieval, consultation and collaboration, referencing, and risk analysis. As prospective HIA users, they also confirmed that information is not routinely sought to mitigate the potential negative health impacts of education-based policies. MOH respondents (N=8) identified the bureaucratic hierarchy as the brokering mechanism for inter-ministerial policy development. As prospective HIA stewards, they also confirmed that the ministry does not proactively flag the potential negative health impacts of non-health sector policies. Finally, ‘lessons learned’ from case articles specific to Québec (n=12) and South Australia (n=17) identified the political factors supporting tool use at different stages of the policy cycle, including agenda setting (‘policy elites’ and ‘political culture’), implementation (‘jurisdiction’), and sustained implementation (‘institutional power’). This work provides important insights into ‘real life’ policymaking. By highlighting existing facilitators of and barriers to HIA use, the findings offer a useful starting point from which proponents may tailor context-specific strategies to sustainably implement HIA at the sub-national government level

    Modeling Uncertainty for Reliable Probabilistic Modeling in Deep Learning and Beyond

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    [ES] Esta tesis se enmarca en la intersección entre las técnicas modernas de Machine Learning, como las Redes Neuronales Profundas, y el modelado probabilístico confiable. En muchas aplicaciones, no solo nos importa la predicción hecha por un modelo (por ejemplo esta imagen de pulmón presenta cáncer) sino también la confianza que tiene el modelo para hacer esta predicción (por ejemplo esta imagen de pulmón presenta cáncer con 67% probabilidad). En tales aplicaciones, el modelo ayuda al tomador de decisiones (en este caso un médico) a tomar la decisión final. Como consecuencia, es necesario que las probabilidades proporcionadas por un modelo reflejen las proporciones reales presentes en el conjunto al que se ha asignado dichas probabilidades; de lo contrario, el modelo es inútil en la práctica. Cuando esto sucede, decimos que un modelo está perfectamente calibrado. En esta tesis se exploran tres vias para proveer modelos más calibrados. Primero se muestra como calibrar modelos de manera implicita, que son descalibrados por técnicas de aumentación de datos. Se introduce una función de coste que resuelve esta descalibración tomando como partida las ideas derivadas de la toma de decisiones con la regla de Bayes. Segundo, se muestra como calibrar modelos utilizando una etapa de post calibración implementada con una red neuronal Bayesiana. Finalmente, y en base a las limitaciones estudiadas en la red neuronal Bayesiana, que hipotetizamos que se basan en un prior mispecificado, se introduce un nuevo proceso estocástico que sirve como distribución a priori en un problema de inferencia Bayesiana.[CA] Aquesta tesi s'emmarca en la intersecció entre les tècniques modernes de Machine Learning, com ara les Xarxes Neuronals Profundes, i el modelatge probabilístic fiable. En moltes aplicacions, no només ens importa la predicció feta per un model (per ejemplem aquesta imatge de pulmó presenta càncer) sinó també la confiança que té el model per fer aquesta predicció (per exemple aquesta imatge de pulmó presenta càncer amb 67% probabilitat). En aquestes aplicacions, el model ajuda el prenedor de decisions (en aquest cas un metge) a prendre la decisió final. Com a conseqüència, cal que les probabilitats proporcionades per un model reflecteixin les proporcions reals presents en el conjunt a què s'han assignat aquestes probabilitats; altrament, el model és inútil a la pràctica. Quan això passa, diem que un model està perfectament calibrat. En aquesta tesi s'exploren tres vies per proveir models més calibrats. Primer es mostra com calibrar models de manera implícita, que són descalibrats per tècniques d'augmentació de dades. S'introdueix una funció de cost que resol aquesta descalibració prenent com a partida les idees derivades de la presa de decisions amb la regla de Bayes. Segon, es mostra com calibrar models utilitzant una etapa de post calibratge implementada amb una xarxa neuronal Bayesiana. Finalment, i segons les limitacions estudiades a la xarxa neuronal Bayesiana, que es basen en un prior mispecificat, s'introdueix un nou procés estocàstic que serveix com a distribució a priori en un problema d'inferència Bayesiana.[EN] This thesis is framed at the intersection between modern Machine Learning techniques, such as Deep Neural Networks, and reliable probabilistic modeling. In many machine learning applications, we do not only care about the prediction made by a model (e.g. this lung image presents cancer) but also in how confident is the model in making this prediction (e.g. this lung image presents cancer with 67% probability). In such applications, the model assists the decision-maker (in this case a doctor) towards making the final decision. As a consequence, one needs that the probabilities provided by a model reflects the true underlying set of outcomes, otherwise the model is useless in practice. When this happens, we say that a model is perfectly calibrated. In this thesis three ways are explored to provide more calibrated models. First, it is shown how to calibrate models implicitly, which are decalibrated by data augmentation techniques. A cost function is introduced that solves this decalibration taking as a starting point the ideas derived from decision making with Bayes' rule. Second, it shows how to calibrate models using a post-calibration stage implemented with a Bayesian neural network. Finally, and based on the limitations studied in the Bayesian neural network, which we hypothesize that came from a mispecified prior, a new stochastic process is introduced that serves as a priori distribution in a Bayesian inference problem.Maroñas Molano, J. (2022). Modeling Uncertainty for Reliable Probabilistic Modeling in Deep Learning and Beyond [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/181582TESI

    Modelling and Solving the Single-Airport Slot Allocation Problem

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    Currently, there are about 200 overly congested airports where airport capacity does not suffice to accommodate airline demand. These airports play a critical role in the global air transport system since they concern 40% of global passenger demand and act as a bottleneck for the entire air transport system. This imbalance between airport capacity and airline demand leads to excessive delays, as well as multi-billion economic, and huge environmental and societal costs. Concurrently, the implementation of airport capacity expansion projects requires time, space and is subject to significant resistance from local communities. As a short to medium-term response, Airport Slot Allocation (ASA) has been used as the main demand management mechanism. The main goal of this thesis is to improve ASA decision-making through the proposition of models and algorithms that provide enhanced ASA decision support. In doing so, this thesis is organised into three distinct chapters that shed light on the following questions (I–V), which remain untapped by the existing literature. In parentheses, we identify the chapters of this thesis that relate to each research question. I. How to improve the modelling of airline demand flexibility and the utility that each airline assigns to each available airport slot? (Chapters 2 and 4) II. How can one model the dynamic and endogenous adaptation of the airport’s landside and airside infrastructure to the characteristics of airline demand? (Chapter 2) III. How to consider operational delays in strategic ASA decision-making? (Chapter 3) IV. How to involve the pertinent stakeholders into the ASA decision-making process to select a commonly agreed schedule; and how can one reduce the inherent decision-complexity without compromising the quality and diversity of the schedules presented to the decision-makers? (Chapter 3) V. Given that the ASA process involves airlines (submitting requests for slots) and coordinators (assigning slots to requests based on a set of rules and priorities), how can one jointly consider the interactions between these two sides to improve ASA decision-making? (Chapter 4) With regards to research questions (I) and (II), the thesis proposes a Mixed Integer Programming (MIP) model that considers airlines’ timing flexibility (research question I) and constraints that enable the dynamic and endogenous allocation of the airport’s resources (research question II). The proposed modelling variant addresses several additional problem characteristics and policy rules, and considers multiple efficiency objectives, while integrating all constraints that may affect airport slot scheduling decisions, including the asynchronous use of the different airport resources (runway, aprons, passenger terminal) and the endogenous consideration of the capabilities of the airport’s infrastructure to adapt to the airline demand’s characteristics and the aircraft/flight type associated with each request. The proposed model is integrated into a two-stage solution approach that considers all primary and several secondary policy rules of ASA. New combinatorial results and valid tightening inequalities that facilitate the solution of the problem are proposed and implemented. An extension of the above MIP model that considers the trade-offs among schedule displacement, maximum displacement, and the number of displaced requests, is integrated into a multi-objective solution framework. The proposed framework holistically considers the preferences of all ASA stakeholder groups (research question IV) concerning multiple performance metrics and models the operational delays associated with each airport schedule (research question III). The delays of each schedule/solution are macroscopically estimated, and a subtractive clustering algorithm and a parameter tuning routine reduce the inherent decision complexity by pruning non-dominated solutions without compromising the representativeness of the alternatives offered to the decision-makers (research question IV). Following the determination of the representative set, the expected delay estimates of each schedule are further refined by considering the whole airfield’s operations, the landside, and the airside infrastructure. The representative schedules are ranked based on the preferences of all ASA stakeholder groups concerning each schedule’s displacement-related and operational-delay performance. Finally, in considering the interactions between airlines’ timing flexibility and utility, and the policy-based priorities assigned by the coordinator to each request (research question V), the thesis models the ASA problem as a two-sided matching game and provides guarantees on the stability of the proposed schedules. A Stable Airport Slot Allocation Model (SASAM) capitalises on the flexibility considerations introduced for addressing research question (I) through the exploitation of data submitted by the airlines during the ASA process and provides functions that proxy each request’s value considering both the airlines’ timing flexibility for each submitted request and the requests’ prioritisation by the coordinators when considering the policy rules defining the ASA process. The thesis argues on the compliance of the proposed functions with the primary regulatory requirements of the ASA process and demonstrates their applicability for different types of slot requests. SASAM guarantees stability through sets of inequalities that prune allocations blocking the formation of stable schedules. A multi-objective Deferred-Acceptance (DA) algorithm guaranteeing the stability of each generated schedule is developed. The algorithm can generate all stable non-dominated points by considering the trade-off between the spilled airline and passenger demand and maximum displacement. The work conducted in this thesis addresses several problem characteristics and sheds light on their implications for ASA decision-making, hence having the potential to improve ASA decision-making. Our findings suggest that the consideration of airlines’ timing flexibility (research question I) results in improved capacity utilisation and scheduling efficiency. The endogenous consideration of the ability of the airport’s infrastructure to adapt to the characteristics of airline demand (research question II) enables a more efficient representation of airport declared capacity that results in the scheduling of additional requests. The concurrent consideration of airlines’ timing flexibility and the endogenous adaptation of airport resources to airline demand achieves an improved alignment between the airport infrastructure and the characteristics of airline demand, ergo proposing schedules of improved efficiency. The modelling and evaluation of the peak operational delays associated with the different airport schedules (research question III) provides allows the study of the implications of strategic ASA decision-making for operations and quantifies the impact of the airport’s declared capacity on each schedule’s operational performance. In considering the preferences of the relevant ASA stakeholders (airlines, coordinators, airport, and air traffic authorities) concerning multiple operational and strategic ASA efficiency metrics (research question IV) the thesis assesses the impact of alternative preference considerations and indicates a commonly preferred schedule that balances the stakeholders’ preferences. The proposition of representative subsets of alternative schedules reduces decision-complexity without significantly compromising the quality of the alternatives offered to the decision-making process (research question IV). The modelling of the ASA as a two-sided matching game (research question V), results in stable schedules consisting of request-to-slot assignments that provide no incentive to airlines and coordinators to reject or alter the proposed timings. Furthermore, the proposition of stable schedules results in more intensive use of airport capacity, while simultaneously improving scheduling efficiency. The models and algorithms developed as part of this thesis are tested using airline requests and airport capacity data from coordinated airports. Computational results that are relevant to the context of the considered airport instances provide evidence on the potential improvements for the current ASA process and facilitate data-driven policy and decision-making. In particular, with regards to the alignment of airline demand with the capabilities of the airport’s infrastructure (questions I and II), computational results report improved slot allocation efficiency and airport capacity utilisation, which for the considered airport instance translate to improvements ranging between 5-24% for various schedule performance metrics. In reducing the difficulty associated with the assessment of multiple ASA solutions by the stakeholders (question IV), instance-specific results suggest reductions to the number of alternative schedules by 87%, while maintaining the quality of the solutions presented to the stakeholders above 70% (expressed in relation to the initially considered set of schedules). Meanwhile, computational results suggest that the concurrent consideration of ASA stakeholders’ preferences (research question IV) with regards to both operational (research question III) and strategic performance metrics leads to alternative airport slot scheduling solutions that inform on the trade-offs between the schedules’ operational and strategic performance and the stakeholders’ preferences. Concerning research question (V), the application of SASAM and the DA algorithm suggest improvements to the number of unaccommodated flights and passengers (13 and 40% improvements) at the expense of requests concerning fewer passengers and days of operations (increasing the number of rejected requests by 1.2% in relation to the total number of submitted requests). The research conducted in this thesis aids in the identification of limitations that should be addressed by future studies to further improve ASA decision-making. First, the thesis focuses on exact solution approaches that consider the landside and airside infrastructure of the airport and generate multiple schedules. The proposition of pre-processing techniques that identify the bottleneck of the airport’s capacity, i.e., landside and/or airside, can be used to reduce the size of the proposed formulations and improve the required computational times. Meanwhile, the development of multi-objective heuristic algorithms that consider several problem characteristics and generate multiple efficient schedules in reasonable computational times, could extend the capabilities of the models propositioned in this thesis and provide decision support for some of the world’s most congested airports. Furthermore, the thesis models and evaluates the operational implications of strategic airport slot scheduling decisions. The explicit consideration of operational delays as an objective in ASA optimisation models and algorithms is an issue that merits investigation since it may further improve the operational performance of the generated schedules. In accordance with current practice, the models proposed in this work have considered deterministic capacity parameters. Perhaps, future research could propose formulations that consider stochastic representations of airport declared capacity and improve strategic ASA decision-making through the anticipation of operational uncertainty and weather-induced capacity reductions. Finally, in modelling airlines’ utility for each submitted request and available time slot the thesis proposes time-dependent functions that utilise available data to approximate airlines’ scheduling preferences. Future studies wishing to improve the accuracy of the proposed functions could utilise commercial data sources that provide route-specific information; or in cases that such data is unavailable, employ data mining and machine learning methodologies to extract airlines’ time-dependent utility and preferences

    Towards a non-equilibrium thermodynamic theory of ecosystem assembly and development

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    Non-equilibrium thermodynamics has had a significant historic influence on the development of theoretical ecology, even informing the very concept of an ecosystem. Much of this influence has manifested as proposed extremal principles. These principles hold that systems will tend to maximise certain thermodynamic quantities, subject to the other constraints they operate under. A particularly notable extremal principle is the maximum entropy production principle (MaxEPP); that systems maximise their rate of entropy production. However, these principles are not robustly based in physical theory, and suffer from treating complex ecosystems in an extremely coarse manner. To address this gap, this thesis derives a limited but physically justified extremal principle, as well as carrying out a detailed investigation of the impact of non-equilibrium thermodynamic constraints on the assembly of microbial communities. The extremal principle we obtain pertains to the switching between states in simple bistable systems, with switching paths that generate more entropy being favoured. Our detailed investigation into microbial communities involved developing a novel thermodynamic microbial community model, using which we found the rate of ecosystem development to be set by the availability of free-energy. Further investigation was carried out using this model, demonstrating the way that trade-offs emerging from fundamental thermodynamic constraints impact the dynamics of assembling microbial communities. Taken together our results demonstrate that theory can be developed from non-equilibrium thermodynamics, that is both ecologically relevant and physically well grounded. We find that broad extremal principles are unlikely to be obtained, absent significant advances in the field of stochastic thermodynamics, limiting their applicability to ecology. However, we find that detailed consideration of the non-equilibrium thermodynamic mechanisms that impact microbial communities can broaden our understanding of their assembly and functioning.Open Acces

    A suite of quantum algorithms for the shortestvector problem

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    Crytography has come to be an essential part of the cybersecurity infrastructure that provides a safe environment for communications in an increasingly connected world. The advent of quantum computing poses a threat to the foundations of the current widely-used cryptographic model, due to the breaking of most of the cryptographic algorithms used to provide confidentiality, authenticity, and more. Consequently a new set of cryptographic protocols have been designed to be secure against quantum computers, and are collectively known as post-quantum cryptography (PQC). A forerunner among PQC is lattice-based cryptography, whose security relies upon the hardness of a number of closely related mathematical problems, one of which is known as the shortest vector problem (SVP). In this thesis I describe a suite of quantum algorithms that utilize the energy minimization principle to attack the shortest vector problem. The algorithms outlined span the gate-model and continuous time quantum computing, and explore methods of parameter optimization via variational methods, which are thought to be effective on near-term quantum computers. The performance of the algorithms are analyzed numerically, analytically, and on quantum hardware where possible. I explain how the results obtained in the pursuit of solving SVP apply more broadly to quantum algorithms seeking to solve general real-world problems; minimize the effect of noise on imperfect hardware; and improve efficiency of parameter optimization.Open Acces

    GENDERED EMBODIMENT, STABILITY AND CHANGE: WOMEN’S WEIGHTLIFTING AS A TOOL FOR RECOVERY FROM EATING DISORDERS

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    This thesis explores the everyday embodied experiences of women who use amateur weightlifting as a vehicle for recovery from eating disorders. Within online spaces and on social media, women frequently share their experiences of using weightlifting to overcome issues relating to disordered eating, body image, and mental health. In particular, women with a history of eating disorders credit weightlifting to be integral to their recovery journey. However, there is a dearth of research on women’s experiences with exercise during eating disorder recovery and no research that identifies weightlifting as beneficial to this process. To the contrary, discursive links are drawn between the practices of self-surveillance exercised by both eating disorder sufferers and weightlifters alike. In this regard, engagement with weightlifting during eating disorder recovery may signal the transferal of pathology from one set of behaviours to another. That is, from disordered eating to rigid and self-regulatory exercise routines. This thesis examines how women subjectively navigate and make sense of this pathologisation. The data for this research comes from longitudinal semi-structured interviews and photo elicitation with 19 women, living in the United Kingdom, who engaged in weightlifting during their eating disorder recovery. In addition, to build up a holistic picture and to explore how this phenomenon also ‘takes place’ online, I conducted a netnography of the overlapping subcultures of female weightlifting and eating disorder recovery on Instagram. Women’s standpoint theory and interpretative phenomenological analysis are combined to form the underpinning theoretical and analytical tools used to engage with these three rich data sets. Moreover, throughout I draw on an eclectic range of disciplinary perspectives, in order to bring together multiple fields of research and develop novel theoretical frameworks. In the findings, I argue that women’s experiences using weightlifting as a tool for recovery from eating disorders manifests in an embodied sense of multiplicity. In this sense, understandings of the body that are often viewed as ontologically distinct (muscularity/thinness/fatness) hang-together at once in the lived experience of a single individual. I argue that women, particularly those who have previously struggled with an eating disorder, are too readily positioned as vulnerable to media and representation. To theoretically combat these ideas regarding women’s assumed passivity, I develop the concept of ‘digital pruning’ to account for women’s agency in relation to new media. I contend that weightlifting offers women in recovery from eating disorders a new framework for approaching eating and exercise. Specifically, weightlifting’s norms and values legitimate occupying a larger body, which gives women in recovery permission to eat and gain-weight in a way that is both culturally sanctioned and health-promoting. Finally, I explore identity transformation as a specific tenet of recovery from eating disorders. I argue that, on social media, recovery identities are characterised by personal empowerment, resilience, and independence. While offline, quieter and less culturally glorified aspects of recovery (such as relationships of care) are central to women’s accounts of developing a new sense of self as they transition away from an eating disorder identity. In summary, this thesis is an examination of the ways in which women strategically navigate pathology in relation to their bodies, social media, food/exercise practices, and identity. I argue that women develop a set of ‘DIY’ recovery practices that allow them to consciously channel and draw on their negative experiences with eating disorders, to develop new ways of living that serve their overall wellbeing. Weightlifting is integral to this process, as it provides women transitioning out of this difficult phase in their lives with new ways of relating to their bodies and of being in the world. I situate this phenomenon within a neoliberal socio-political climate in which individuals are required to take personal responsibility for their mental health and wellbeing, despite living within conditions which are not conducive to recovery

    Improving the Logarithmic Accuracy of the Angular-Ordered Parton Shower

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    Monte Carlo event generators are a key tool for making theoretical predictions that can be compared with the results of collider experiments, our most accurate probes of fundamental particle physics. New developments in the way parton shower accuracy is assessed have led us to re-examine the accuracy of the angular-ordered parton shower in the Herwig 7 event generator, focussing on the way recoil is handled after successive emissions. We first discuss how the evolution variable is defined in the Herwig angular-ordered shower and how the choice of this definition determines the recoil scheme. We then show how the recoil scheme can affect the logarithmic accuracy of final-state radiation produced by the algorithm. As part of this investigation we consider a new interpretation of the evolution variable intended to mitigate problems with previous iterations of the shower. To test this, simulated events for each scheme are compared with experimental data from both LEP and the LHC. Next we extend our analysis to initial-state radiation and perform the same process of assessing the logarithmic accuracy of different interpretations of the evolution variable. This time, we compare simulated events for each scheme with LHC data for the vector boson production. Additionally, we consider the impact that the choice of NLO matching scheme has on the accuracy of these simulations, with reference to the same LHC data
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