20,471 research outputs found

    Modelling uncertainties for measurements of the H → γγ Channel with the ATLAS Detector at the LHC

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    The Higgs boson to diphoton (H → γγ) branching ratio is only 0.227 %, but this final state has yielded some of the most precise measurements of the particle. As measurements of the Higgs boson become increasingly precise, greater import is placed on the factors that constitute the uncertainty. Reducing the effects of these uncertainties requires an understanding of their causes. The research presented in this thesis aims to illuminate how uncertainties on simulation modelling are determined and proffers novel techniques in deriving them. The upgrade of the FastCaloSim tool is described, used for simulating events in the ATLAS calorimeter at a rate far exceeding the nominal detector simulation, Geant4. The integration of a method that allows the toolbox to emulate the accordion geometry of the liquid argon calorimeters is detailed. This tool allows for the production of larger samples while using significantly fewer computing resources. A measurement of the total Higgs boson production cross-section multiplied by the diphoton branching ratio (σ × Bγγ) is presented, where this value was determined to be (σ × Bγγ)obs = 127 ± 7 (stat.) ± 7 (syst.) fb, within agreement with the Standard Model prediction. The signal and background shape modelling is described, and the contribution of the background modelling uncertainty to the total uncertainty ranges from 18–2.4 %, depending on the Higgs boson production mechanism. A method for estimating the number of events in a Monte Carlo background sample required to model the shape is detailed. It was found that the size of the nominal γγ background events sample required a multiplicative increase by a factor of 3.60 to adequately model the background with a confidence level of 68 %, or a factor of 7.20 for a confidence level of 95 %. Based on this estimate, 0.5 billion additional simulated events were produced, substantially reducing the background modelling uncertainty. A technique is detailed for emulating the effects of Monte Carlo event generator differences using multivariate reweighting. The technique is used to estimate the event generator uncertainty on the signal modelling of tHqb events, improving the reliability of estimating the tHqb production cross-section. Then this multivariate reweighting technique is used to estimate the generator modelling uncertainties on background V γγ samples for the first time. The estimated uncertainties were found to be covered by the currently assumed background modelling uncertainty

    Image classification over unknown and anomalous domains

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    A longstanding goal in computer vision research is to develop methods that are simultaneously applicable to a broad range of prediction problems. In contrast to this, models often perform best when they are specialized to some task or data type. This thesis investigates the challenges of learning models that generalize well over multiple unknown or anomalous modes and domains in data, and presents new solutions for learning robustly in this setting. Initial investigations focus on normalization for distributions that contain multiple sources (e.g. images in different styles like cartoons or photos). Experiments demonstrate the extent to which existing modules, batch normalization in particular, struggle with such heterogeneous data, and a new solution is proposed that can better handle data from multiple visual modes, using differing sample statistics for each. While ideas to counter the overspecialization of models have been formulated in sub-disciplines of transfer learning, e.g. multi-domain and multi-task learning, these usually rely on the existence of meta information, such as task or domain labels. Relaxing this assumption gives rise to a new transfer learning setting, called latent domain learning in this thesis, in which training and inference are carried out over data from multiple visual domains, without domain-level annotations. Customized solutions are required for this, as the performance of standard models degrades: a new data augmentation technique that interpolates between latent domains in an unsupervised way is presented, alongside a dedicated module that sparsely accounts for hidden domains in data, without requiring domain labels to do so. In addition, the thesis studies the problem of classifying previously unseen or anomalous modes in data, a fundamental problem in one-class learning, and anomaly detection in particular. While recent ideas have been focused on developing self-supervised solutions for the one-class setting, in this thesis new methods based on transfer learning are formulated. Extensive experimental evidence demonstrates that a transfer-based perspective benefits new problems that have recently been proposed in anomaly detection literature, in particular challenging semantic detection tasks

    Towards a more just refuge regime: quotas, markets and a fair share

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    The international refugee regime is beset by two problems: Responsibility for refuge falls disproportionately on a few states and many owed refuge do not get it. In this work, I explore remedies to these problems. One is a quota distribution wherein states are distributed responsibilities via allotment. Another is a marketized quota system wherein states are free to buy and sell their allotments with others. I explore these in three parts. In Part 1, I develop the prime principles upon which a just regime is built and with which alternatives can be adjudicated. The first and most important principle – ‘Justice for Refugees’ – stipulates that a just regime provides refuge for all who have a basic interest in it. The second principle – ‘Justice for States’ – stipulates that a just distribution of refuge responsibilities among states is one that is capacity considerate. In Part 2, I take up several vexing questions regarding the distribution of refuge responsibilities among states in a collective effort. First, what is a state’s ‘fair share’? The answer requires the determination of some logic – some metric – with which a distribution is determined. I argue that one popular method in the political theory literature – a GDP-based distribution – is normatively unsatisfactory. In its place, I posit several alternative metrics that are more attuned with the principles of justice but absent in the political theory literature: GDP adjusted for Purchasing Power Parity and the Human Development Index. I offer an exploration of both these. Second, are states required to ‘take up the slack’ left by defaulting peers? Here, I argue that duties of help remain intact in cases of partial compliance among states in the refuge regime, but that political concerns may require that such duties be applied with caution. I submit that a market instrument offers one practical solution to this problem, as well as other advantages. In Part 3, I take aim at marketization and grapple with its many pitfalls: That marketization is commodifying, that it is corrupting, and that it offers little advantage in providing quality protection for refugees. In addition to these, I apply a framework of moral markets developed by Debra Satz. I argue that a refuge market may satisfy Justice Among States, but that it is violative of the refugees’ welfare interest in remaining free of degrading and discriminatory treatment

    Proof of Concept of Therapeutic Gene Modulation of MBNL1/2 in Myotonic Dystrophy

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    La distrofia miotónica tipo 1 es una enfermedad genética rara multisistémica que afecta a 1 de cada 3000-8000 personas. La causa molecular de la enfermedad proviene de repeticiones tóxicas “CTG” en el gen DMPK (DM Protein Kinase). Tras la transcripción, estas repeticiones forman una estructura de horquilla que se une con alta afinidad a la familia de proteínas MBNL (Muscleblind-like) que agota su función de regulación de la poliadenilación y el splicing alternativo postranscripcional en numerosos transcritos. La pérdida de función de MBNL provoca una cascada de efectos posteriores, que eventualmente conducen a síntomas clínicos que incluyen miotonía, debilidad y atrofia muscular, cataratas, disfunción cardíaca y trastorno cognitivo. La restauración de la función de la proteína MBNL es clave para aliviar los síntomas debilitantes de esta enfermedad. Se han utilizado oligonucleótidos antisentido (AON) para apuntar a las repeticiones de DMPK y liberar MBNL del secuestro, lo que da como resultado resultados terapéuticos prometedores en modelos celulares y animales de la enfermedad. Otro factor que interviene en la pérdida de función de las proteínas MBNL son los miRNAs que regulan su traducción. Aquí se muestra el uso de AON dirigidos a la actividad de miR-23b y miR-218, que se ha demostrado previamente que regulan directamente MBNL1 y MBNL2. Estos antimiRs recibieron modificaciones FANA para aumentar su entrega en las células y reducir la toxicidad. También se probaron los AON, denominados blockmiRs, que se unen de manera complementaria a los sitios de unión confirmados de miR-23b y miR-218 en los 3'-UTR de las transcripciones de MBNL1 y MBNL2. De esta manera, los miRNAs no pueden unirse y regular la traducción de MBNL, lo que aumenta la cantidad de proteína MBNL producida en una célula deficiente. Aquí se propone el uso de AON de nuevo diseño dirigidos a la actividad de miR-23b y miR-218 para regular MBNL1 y MBNL2 a través de (1) exploración del bloqueo de miRNA a través de FANA-antimiR AON in vitro, (2) exploración del bloqueo del sitio de unión de miRNA a través de la estrategia blockmiR in vitro e in vivo con el uso de modificaciones químicas de LNA, y (3) mejora de la química de la estrategia blockmiR mediante el uso de tecnología de péptidos de penetración celular in vitro e in vivo.Myotonic Dystrophy Type 1 is a multi-systemic rare genetic disease affecting 1 in 3000-8000 people. The molecular cause of the disease stems from toxic “CTG” repetitions in the DMPK (DM Protein Kinase) gene. Upon transcription, these repetitions form a hairpin structure that binds with high affinity to the MBNL (Muscleblind-like) family of proteins depleting their function of post-transcriptional alternative splicing and polyadenylation regulation on numerous transcripts. MBNL loss-of-function causes a cascade of downstream effects, which eventually lead to clinical symptoms including myotonia, muscle weakness and atrophy, cataracts, cardiac dysfunction, and cognitive disorder. The restoration of MBNL protein function is key to relieving the debilitating symptoms of this disease. Antisense oligonucleotides (AONs) have been used to target the DMPK repeats and release MBNL from sequestration resulting in promising therapeutic results in cellular and animal models of the disease. Another factor playing a role in the loss-of-function of MBNL proteins are the miRNAs that regulate their translation. Here is shown the use of AONs targeting miR-23b and miR-218 activity, which have been previously shown to directly regulate MBNL1 and MBNL2. These antimiRs were given FANA modifications to increase their delivery in cells and lower toxicity. Also tested are AONs, termed blockmiRs, that complementary bind to the confirmed binding sites of miR-23b and miR-218 in the 3’-UTRs of MBNL1 and MBNL2 transcripts. In this way, the miRNAs are unable to bind and regulate the translation of MBNL thereby augmenting the amount of MBNL protein made in an otherwise deficient cell. Proposed here is the use of newly designed AONs targeting miR-23b and miR-218 activity in order to regulate MBNL1 and MBNL2 through (1) exploration of miRNA blocking through FANA-antimiR AONs in vitro, (2) exploration of miRNA binding site blocking through blockmiR strategy in vitro and in vivo with the use of LNA chemical modifications, and (3) improvement of the chemistry of the blockmiR strategy through the use of cell penetrating peptide technology in vitro and in vivo

    Exploring the Structure of Scattering Amplitudes in Quantum Field Theory: Scattering Equations, On-Shell Diagrams and Ambitwistor String Models in Gauge Theory and Gravity

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    In this thesis I analyse the structure of scattering amplitudes in super-symmetric gauge and gravitational theories in four dimensional spacetime, starting with a detailed review of background material accessible to a non-expert. I then analyse the 4D scattering equations, developing the theory of how they can be used to express scattering amplitudes at tree level. I go on to explain how the equations can be solved numerically using a Monte Carlo algorithm, and introduce my Mathematica package treeamps4dJAF which performs these calculations. Next I analyse the relation between the 4D scattering equations and on-shell diagrams in N = 4 super Yang-Mills, which provides a new perspective on the tree level amplitudes of the theory. I apply a similar analysis to N = 8 supergravity, developing the theory of on-shell diagrams to derive new Grassmannian integral formulae for the amplitudes of the theory. In both theories I derive a new worldsheet expression for the 4 point one loop amplitude supported on 4D scattering equations. Finally I use 4D ambitwistor string theory to analyse scattering amplitudes in N = 4 conformal supergravity, deriving new worldsheet formulae for both plane wave and non-plane wave amplitudes supported on 4D scattering equations. I introduce a new prescription to calculate the derivatives of on-shell variables with respect to momenta, and I use this to show that certain non-plane wave amplitudes can be calculated as momentum derivatives of amplitudes with plane wave states

    BECOMEBECOME - A TRANSDISCIPLINARY METHODOLOGY BASED ON INFORMATION ABOUT THE OBSERVER

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    ABSTRACT Andrea T. R. Traldi BECOMEBECOME A Transdisciplinary Methodology Based on Information about the Observer The present research dissertation has been developed with the intention to provide practical strategies and discover new intellectual operations which can be used to generate Transdisciplinary insight. For this reason, this thesis creates access to new knowledge at different scales. Firstly, as it pertains to the scale of new knowledge generated by those who attend Becomebecome events. The open-source nature of the Becomebecome methodology makes it possible for participants in Becomebecome workshops, training programmes and residencies to generate new insight about the specific project they are working on, which then reinforce and expand the foundational principles of the theoretical background. Secondly, as it pertains to the scale of the Becomebecome framework, which remains independent of location and moment in time. The method proposed to access Transdisciplinary knowledge constitutes new knowledge in itself because the sequence of activities, described as physical and mental procedures and listed as essential criteria, have never been found organised 6 in such a specific order before. It is indeed the order in time, i.e. the sequence of the ideas and activities proposed, which allows one to transform Disciplinary knowledge via a new Transdisciplinary frame of reference. Lastly, new knowledge about Transdisciplinarity as a field of study is created as a consequence of the heretofore listed two processes. The first part of the thesis is designated ‘Becomebecome Theory’ and focuses on the theoretical background and the intellectual operations necessary to support the creation of new Transdisciplinary knowledge. The second part of the thesis is designated ‘Becomebecome Practice’ and provides practical examples of the application of such operations. Crucially, the theoretical model described as the foundation for the Becomebecome methodology (Becomebecome Theory) is process-based and constantly checked against the insight generated through Becomebecome Practice. To this effect, ‘information about the observer’ is proposed as a key notion which binds together Transdisciplinary resources from several studies in the hard sciences and humanities. It is a concept that enables understanding about why and how information that is generated through Becomebecome Practice is considered of paramount importance for establishing the reference parameters necessary to access Transdisciplinary insight which is meaningful to a specific project, a specific person, or a specific moment in time

    Understanding the Relationship among Durable Goods, Academic Achievement, and School Attendance in Colombia

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    A joint report from the United Nations Development Program and the Oxford Poverty and Human Development Initiative indicates that while the number of people living with less than 1.90adaydeclinedglobally,droppingfrom2billionin1990to736millionin2015,thenumberofpeoplewhoexperiencednonincomepovertyreached1.3billionin2020.Nonincomepoverty,referredtoasmultidimensionalpoverty,assessestheextenttowhichpeoplearedeprivedfromaccessingbasicservicessuchashealth,education,orattainingdecentlivingstandards,despitehavingincomelevelswellabove1.90 a day declined globally, dropping from 2 billion in 1990 to 736 million in 2015, the number of people who experienced non-income poverty reached 1.3 billion in 2020. Non-income poverty, referred to as multidimensional poverty, assesses the extent to which people are deprived from accessing basic services such as health, education, or attaining decent living standards, despite having income levels well above 1.90. Research on development and welfare economics points to assets as the missing piece in the poverty puzzle because they can build capacity. In general, assets can be used to generate income or to enhance quality of life. Income-generating assets such as bonds, credit, or home ownership help people gain economic stability, acquire other assets, and prepare for economic shocks. Quality-of-life-enhancing assets help people improve their living standards, develop agency, and participate in political as well as in social life. Examples of quality-of-life-enhancing assets include education, social capital, and durable goods such as TVs or computers. Most research on assets examines the relationship either between financial assets and poverty or between financial assets and education. An exploration of durable goods and education was the focus of this dissertation. Although not a nascent field, most studies in this area have focused on analyzing how durable goods relate to academic achievement and school attendance mainly in African and Asian countries. From a methodological standpoint, these studies have modeled durable goods utilizing a binary approach, where ownership of durable goods is measured as possession of any durable good, or as an index, using principal component analysis (PCA), which research suggests is not the most robust method for index creation. Such methodological decisions have provided only a partial understanding of the relationship between durable goods and education. For example, findings indicate that possession of durable goods improves achievement in reading, but not in math. However, further research is needed to assess whether different types of durable goods have differential effects on educational outcomes. Hence, this study explored the relationship among durable goods, academic achievement, and school attendance in Colombia through three methodological approaches to operationalize durable goods: inventory, attributional, and index approaches. Data come from the 2017 SABER test, a nation-wide examination that assesses reading and math skills, for fifth and ninth grade students, (N = 621,218). Students with complete durable goods information (N = 364,436) were included. This research added to the existing literature on this field by using different methodological approaches to model durable goods, including the construction of a durable goods index employing exploratory factor analysis (EFA), and by expanding the geographic scope to Latin America. By using hierarchical linear and nonlinear modeling, this study found that, overall, durable goods were positively associated with reading and math outcomes, particularly for fifth graders. Similarly, results indicated that students whose families owned washing machines, computers, or who had Internet access were more likely to go to school

    Constraint-Aware and Efficiency-Aware Control of Air-Path in Fuel Cell Vehicles

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    Fuel cell technology offers the potential for clean, efficient, robust energy productionfor both stationary and mobile applications. But without fast and robust control systems, fuel cells cannot hope to maintain real-life efficiencies near enough to their theoretical potential. This work studies control and constraint management techniques to regulate a nonlinear multivariable air-path system for a proton exchange membrane fuel cell (PEMFC). The control objectives are to avoid oxygen starvation, run at the maximum net efficiency, achieve fast tracking of air flow and pressure set-points, and be easy to calibrate. To operate at maximum efficiency, a set-point map is generated for air pressure at the cathode inlet. Considering that the conventional PEMFC system cannot independently control the inlet pressure using only the compressor motor, a new multivariable analysis and control scheme is formulated by considering an electronic throttle body (ETB) valve downstream of the cathode as a new degree of freedom in the control problem. Based on this new configuration of the fuel cell model, an internal model control (IMC) controller is designed with intuitive tuning parameters to simultaneously control airflow and pressure, and achieves a fast and smooth response despite strongly coupled plant dynamics. Further, a reference governor (RG) using a computationally tractable linear prediction model is included with IMC-based Multi-Input Multi-Output (MIMO) controller to satisfy the constraint on oxygen level. Compared with a Single-Input Single-Output (SISO) air-flow control approach, the proposed MIMO control approach demonstrated up to 7.36 percent lower hydrogen fuel consumption
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