Quantum phase transitions (QPT) on the border of magnetism have provided a fertile hunting ground for the discovery of new states of matter, for example; the marginal Fermi Liquid and non Fermi Liquid states as well high T$_C$ cuprate and magnetically mediated superconductivity. In this thesis I present work on three materials in which it may be possible to tune the system through a magnetic QPT with the application of hydrostatic pressure. Although the details of the underlying physics are different in each of the materials, they are linked by the possibility of finding new states on the border of magnetism. Applying hydrostatic pressure, we have suppressed the ferromagnetic (FM) transition in metallic Fe$_2$P to very low temperature and to a potential QPT. Counter-intuitive broadening of the magnetic hysteresis leading up to the FM-AFM QPT may well be a crucial clue as to the nature of the model needed to understand this phase transition. A sharp increase in the quasi-particle scattering cross-section as well as the residual resistivity accompany a departure from the quadratic temperature dependence of the resistivity. This possible deviation from Fermi liquid behaviour is stable over a significant range of temperature. The unexplained upturn in the resistivity of CeGe that accompanies the AFM transition was studied under pressure. Pressure increased the residual resistivity as well as decreasing the relative size of the upturn, but had a moderate effect on the Neel temperature. The insensitivity of the N$\acute e$el temperature to pressure has been compared to its relative sensitivity to applied feld. The existence of the upturn and its evolution with pressure and applied feld can reasonably be argued to be due to the details of the electron band structure in the system. By applying pressure we have drastically reduced the resistivity of the insulating antiferromagnet NiPS$_3$. Concurrent work on FePS$_3$ has shown metallisation under pressure. It seems reasonable to speculate that NiPS$_3$ may also metallise at higher pressure. The energy gap is narrowed in both materials as pressure is increased. Magnetisation measurements have revealed a low temperature upturn indicating some possible ferromagnetic component or proximity to another magnetic state. A peak in the magnetisation is also seen at 45K in zero-feld cooled measurements. Both of these features point to a system with a complex magnetic ground state.

P-values combination procedure for multiple statistical tests is a common data analysis method in many applications including bioinformatics. However, this procedure is nontrivial when input P-values are dependent. For the Fisher€™s combination procedure, a classic method is the Brown€™s Strategy [1, Brown,1975], which is based empirical moment-matching of gamma distribution. In this project, we address a more general family of weighting-andtruncation p-value combination procedures called TFisher. We first study how to extend Brown€™s Strategy to this problem. Then we make further development in two directions. First, instead of using the empirical polynomial model-fitting strategy to find moments, we developed an analytical calculation strategy based on asymptotic approximation. Second, instead of using the gamma distribution to approximate the null distribution of TFisher, we propose to use a mixed gamma distribution or a shifted-mixed gamma distribution. We focus on calculating the one-sided p-value for TFisher, especially the soft-thresholding version of TFisher. Simulations show that our methods much improve the accuracy than the traditional strategy.

Orientadores: Ricardo Rodrigues Urbano, Carlos Rettori / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Física Gleb Wataghin / Made available in DSpace on 2018-09-02T02:37:27Z (GMT). No. of bitstreams: 1 Lesseux_GuilhermeGorgen_D.pdf: 15008598 bytes, checksum: bc12c78a89f519f63d096bf87e9bde84 (MD5) Previous issue date: 2017 / Resumo: Este trabalho teve por objetivo a investigação de sistemas com elétrons fortemente correlacionados via ressonância magnética nuclear (NMR) e eletrônica (ESR). Os seguintes sistemas foram investigados: i) o isolante Kondo SmB6 dopado com impurezas de Er3+ (via ESR), ii) compostos supercondutores à base de FeAs, da família BaFe 2As2 (via NMR) e, iii) o composto férmion pesado CeRhIn5 (via NMR em altos campos magnéticos). O estudo no composto de SmB6 via ESR (9.5 GHz) dos íons de Er3+ revelou um conjunto de quatro transições em baixa temperatura com uma anisotropia que não corresponde à esperada para transições entre níveis de campo cristalino cúbico. Mostramos que o efeito Jahn-Teller (JT) dinâmico associado a vibrações anarmônicas dos íons de Er3+ nos interstícios dos octaedros de B na rede de SmB6 explica a anisotropia das transições finas em baixa temperatura e concorda com o comportamento térmico da intensidade destas linhas de ressonância. Como resultado deste trabalho, ficou então proposta uma nova interpretação dos resultados sob a luz de um efeito de rattling anarmônico dos íons de Er3+ na matriz. Nenhum efeito de isolante topológico tipo Kondo foi evidenciado nos experimentos de ESR. Para os compostos de BaFe2As2 puro e com pouca substituição química (~ 0.5%) de Mn, Co e Cu realizamos um estudo detalhado da evolução das transições de alta temperatura, estrutural e magnética, que ocorrem no diagrama de fase dessa família de supercondutores. Combinando experimentos de NMR para o 75As em altos campos magnéticos, difração de raios-X de alta resolução e calor especíco mostramos que a fase ortorrômbica é estabilizada por flutuações magnéticas via acoplamento magneto elástico e que o ajuste do ângulo entre as ligações de As-Fe-As é o parâmetro mais relevante para a supressão das temperaturas de transição estrutural e magnética. Logo, este ajuste estrutural leva a um rearranjo da ocupação dos orbitais 3d do Fe aumentando a ocupação nos orbitais planares (3dxy), condição fundamental para que a fase supercondutora se forme nestes materiais. Já para o composto de CeRhIn5 investigamos a transição quântica induzida por campo magnético que ocorre em torno de 30 T (~ 1 K) neste material via NMR do 115In em ultra altos campos magnéticos. Observamos uma alteração no knight-shift associado ao In(1), que ocupa os planos de CeIn3. Apesar de nenhum efeito evidente na forma de linha, há um knight-shift líquido de 2.3% através da transição em Bc ~ 30 T. Isto demonstra uma mudança efetiva na densidade de estados no nível de Fermi consistente com a reconstrução da superfície de Fermi em torno de 30 T previamente reportada por medidas de oscilações quânticas. O fato de não se observar alteração de forma de linha espectral em 30 T nos permitiu concluir que a estrutura magnética incomensurável do CeRhIn5 não é drasticamente alterada através da transição o que corrobora com o cenário de um ponto crítico itinerante em torno de 50 T para CeRhIn5. Esta tese demonstra a relevância da técnica de Ressonância Magnética (NMR e ESR) na investigação das propriedades físicas de sistemas com elétrons fortemente correlacionados / Abstract: The present work aimed the investigation of strongly correlated electron systems via nuclear (NMR) and electronic (ESR) magnetic resonance. The following systems were investigated: i) Er3+ doped SmB6 Kondo insulator (via ESR), ii) FeAs-based superconducting compounds, from the BaFe2As2 family (via NMR) and, iii) CeRhIn5 heavy fermion compound (via ultra-high magnetic field NMR). The study on the SmB6 via Er3+ ESR (9.5 GHz) revealed a set of four resonance transitions at low temperature which show an anisotropy that does not correspond to the expected for transitions of pure cubic crystal field levels. We have shown that the dynamic Jahn-Teller (JT) effect associated to anharmonic rattling vibrations of Er3+ ions at the interstitial of the B-octahedron in the SmB6 lattice explains the anisotropy of the narrow lines at low temperature and agrees with the thermal behavior of the intensity of these resonance lines. As a result of this work, we proposed a new interpretation of the results under the light of a anharmonic rattling of the Er3+ ions in the SmB6 lattice. No topological insulator effect was evidenced by our ESR experiments. For the BaFe2As2 undoped and slightly substituted (~ 0.5%) of Mn, Co and Cu compounds we have performed a detailed study of the evolution of the high temperature transitions, structural and magnetic, which occur in the phase diagram of this superconductor family. Combining 75As high field NMR, high resolution X-ray diffraction and specific heat experiments we have shown that the orthorhombic phase is stabilized by magnetic fluctuations via magneto-elastic coupling and that the tuning of the angle of the As-Fe-As bounds is the most relevant parameter to the suppression of the structural and magnetic transition temperatures. Thus, this structural tuning leads to a rearrangement of the occupancy of the Fe-3d orbitals increasing the occupancy of the planar orbital (3dxy), which is a fundamental condition to the formation of the superconducting phase in these materials. Finally, for the CeRhIn5 compound we have investigated the magnetic field-induced quantum transition which occurs around ~ 30 T (~ 1 K) in this material via 115In ultra-high magnetic field NMR. We observed a knight-shift alteration for the In(1), which is sited in the CeIn3 planes. There is a net knight-shift of 2.3% across the transition at Bc ~ 30 T. Although lineshape effects have not been detected, it demonstrates an effective change in the density of states at the Fermi level consistent with a Fermi surface reconstruction around 30 T previously reported by quantum oscillation measurements. We did not observe a change in the spectral lineshape across 30 T and it leads us to the conclusion that the CeRhIn5 incommensurate magnetic structure is not drastically altered across the transition which is consistent with the scenario of an itinerant quantum critical point at 50 T for CeRhIn5. This thesis demonstrates the relevance of the magnetic resonance (NMR and ESR) techniques in the investigation of physical properties of strongly correlated electron systems / Doutorado / Física / Doutor em Ciências / 140837/2013-2 / CNPQ

Este trabalho de tese tem como objetivo principal propor uma abordagem alternativa para analisar dados Birnbaum-Saunders (BS) correlacionados com base em equações de estimação. Da classe ótima de funções de estimação proposta por Crowder (1987), derivamos uma classe ótima para a análise de dados correlacionados em que as distribuições marginais são assumidas log-BS e log-BS-t, respectivamente. Derivamos um processo iterativo para estimação dos parâmetros, métodos de diagnóstico, tais como análise de resíduos, distância de Cook e influência local sob três diferentes esquemas de perturbação: ponderação de casos, perturbação da variável resposta e perturbação individual de covariáveis. Estudos de simulação são desenvolvidos para cada modelo para avaliar as propriedades empíricas dos estimadores dos parâmetros de localização, forma e correlação. A abordagem apresentada é discutida em duas aplicações: o primeiro exemplo referente a um banco de dados sobre a produtividade de capital público nos 48 estados norte-americanos contíguos de 1970 a 1986 e o segundo exemplo referente a um estudo realizado na Escola de Educação Física e Esporte da Universidade de São Paulo (USP) durante 2016 em que 70 corredores foram avaliados em corridas em esteiras em três períodos distintos. / The aim of this thesis is to propose an alternative approach to analyze correlated Birnbaum-Saunders (BS) data based on estimating equations. From the optimal estimating functions class proposed by Crowder (1987), we derive an optimal class for the analysis of correlated data in which the marginal distributions are assumed either log-BS or log-BS-t. It is derived an iterative process, diagnostic procedures such as residual analysis, Cooks distance and local influence under three different perturbation schemes: case-weights, response variable perturbation and single-covariate perturbation. Simulation studies to assess the empirical properties of the parameters estimates are performed for each proposed model. The proposed methodology is discussed in two applications: the first one on a data set of public capital productivity of the contiguous 48 USA states, from 1970 to 1986, and the second data set refers to a study conducted in the School of Physical Education and Sport of the University of São Paulo (USP), during 2016, in which 70 runners were evaluated in running machines races in three periods.

This thesis adopts a survival model to analyze China’s stock market. The data used are the capitalization-weighted stock market index (CSI 300) and the 300 stocks for creating the index. We define the recurrent events using the daily return of the selected stocks and the index. A shared frailty model which incorporates the random effects is then used for analyses since the survival times of individual stocks are correlated. Maximization of penalized likelihood is presented to estimate the parameters in the model. The covariates are selected using the Akaike information criterion (AIC) and the variance inflation factor (VIF) to avoid multicollinearity. The result of analyses show that the general capital, total amount of a stock traded in a day, turnover rate and price book ratio are significant in the shared frailty model for daily stock data.

Methods of estimating parametric and nonparametric components, as well as properties of the corresponding estimators, have been examined in partially linear models by Wahba [1987], Green et al. [1985], Engle et al. [1986], Speckman [1988], Hu et al. [2004], Charnigo et al. [2015] among others. These models are appealing due to their flexibility and wide range of practical applications including the electricity usage study by Engle et al. [1986], gum disease study by Speckman [1988], etc., wherea parametric component explains linear trends and a nonparametric part captures nonlinear relationships. The compound estimator (Charnigo et al. [2015]) has been used to estimate the nonparametric component of such a model with multiple covariates, in conjunction with linear mixed modeling for the parametric component. These authors showed, under a strict orthogonality condition, that parametric and nonparametric component estimators could achieve what appear to be (nearly) optimal rates, even in the presence of subject-specific random effects. We continue with research on partially linear models with subject-specific random intercepts. Inspired by Speckman [1988], we propose estimators of both parametric and nonparametric components of a partially linear model, where consistency is achievable under an orthogonality condition. We also examine a scenario without orthogonality to find that bias could still exist asymptotically. The random intercepts accommodate analysis of individuals on whom repeated measures are taken. We illustrate our estimators in a biomedical case study and assess their finite-sample performance in simulation studies. Jump points have often been found within the domain of nonparametric models (Muller [1992], Loader [1996] and Gijbels et al. [1999]), which may lead to a poor fit when falsely assuming the underlying mean response is continuous. We study a specific type of change-point where the underlying mean response is continuous on both left and right sides of the change-point. We identify the convergence rate of the estimator proposed in Liu [2017] and illustrate the result in simulation studies.

Cette thèse vise à utiliser le t-J Hamiltonian de la corrélation forte pour mieux comprendre la micro-fonctionnalité des scénarios de matériau condensé. Un des problèmes qui existe depuis longtemps est que pour ce type de modèle comme Hubbard Hamiltonian ou t-J Hamiltonian avec une corrélation forte ne peut pas être résolu complètement analytiquement. Par conséquent, quand on aborde ces modèles, il est important de les exploiter de façon numérique. Dans cette thése, nous utiliserons la manière qui s'appelle "Renormalized Mean-Field Theory"(RMFT) pour le t-J Hamiltonian. Grâce à M. Gutzwiller, ce que nous devons faire est simplement de chiffrer des paramètres qui incluent l'influence de la corrélation électronique et de les mettre avant chaque partie du Hamiltonian. Après ce calcul, nous calculerons l'Hamiltonian du champ moyen de manière standard. Ceci sera notre façon principale pour aborder des questions physiques. Ensuite, nous l'appliquerons sur deux systèmes. Le premier est la mystique de supraconducteur à haute température. Après sa découverte il y a 30 ans, on ne peut pas encore définir une théorie pour expliquer sa micromécanique de manière appropriée. Cependant, avec des équipements avancés, on peut faire des expériences correctement et obtenir des résultats exacts. Ces preuves nous facilitent l'élaboration d'une bonne théorie, même s'il est aussi très difficile d'inclure tous les phénomènes ensemble. Nous avons obtenu des résultats et par rapport aux expériences, ils sont similaires qualitativement. Nous montrerons les détails dans le texte. Le deuxième système qui nous intéresse est le mouvement d'électron dans un champ magnétique fort. Le papillon d'Hofstadter et son modèle, l'Hamiltonian de Harper-Hofstadter ont obtenu un grand succès à décrire la mécanique d'électrons libres aux treillis. Donc il est ainsi intéressant de se demander ce qu'il se passera si nous remplaçons des électrons libres avec ceux qui s'interagissent. D'ailleurs, t-J Hamiltonian s'utilise comme bon modèle à le découvrir. Nous allons comparer nos résultats avec ceux de la diagonalisation exacte. Nous proposerons des découvertes intéressantes qui désormais seront réalisées par l'expérience d'atome froide. / This thesis is aiming in utilizing the strongly correlated t-J Hamiltonian for better understanding the microscopic pictures of certain condensed matter scenario. One of the long existing issues in the Hubbard model and its extreme version, t-J model, lies in the fact that there is not an analytical way of solving them. Therefore, when dealing with these models, numerical approaches become very crucial. In this thesis, we will present one of the methods called renormalized mean-field theory (RMFT) and exploit it upon the t-J model. Thanks to the concept proposed by Gutzwiller, all we have to do is to try to include the correlation of electrons, which is mainly the most difficult part, with several renormalization factors. After obtaining the correct form of these factors, we can apply the routine mean-field theory in solving for the Hamiltonian, which is the principle methodology throughout this thesis. Next, the physical systems that we are interested in consist of two parts. The mystery of High-Tc superconductivity comes first. After 30 years of its discovery, people still cannot settle down a complete microscopic theory in describing this exotic phenomenon. However, with more and more experimental equipment with higher accuracy nowadays, lots of behavior of copperoxide superconductor (also known as cuprate) have been revealed. Those discoveries can definitely help us better understand its microscopic mechanism. Therefore, from the theoretical side, to compare the calculated data with experiments leads us to know whether our theory is on the right track or not. We have produced tons of data and made a decent comparison which will be shown in the main text. The second system we are curious about is the mechanism of electrons under magnetic field. The Hofstadter butterfly along with its Hamiltonian, the Harper-Hofstadter model has achieved great success in describing free electrons' movement with lattice present. Thus, it will be also interesting to ask the question: what will happen if the electrons are correlated. Our RMFT for t-J Hamiltonian, by adding an additional phase in the hopping term, happens to serve as a great preliminary model for answering this question. We will compare the results of ours with our collaborators, who solved this model by a different approach, the exact diagonalization(ED). Together with our calculations, we proposed several discoveries which might be realized by the cold atom experiments in the future.

Transition metal oxides have attracted widespread attention due to their broad range of fascinating exotic phenomena such as multiferroicity, superconductivity, colossal magnetoresistance and metal-to-insulator transition. Due to the interplay between spin, charge, lattice and orbital degrees of freedom of strongly correlated d electrons, these physical properties are extremely sensitive to the external perturbations such as magnetic field, charge carrier doping and pressure, which provide a unique chance in search for novel exotic quantum states. Ruthenate systems are a typical strongly correlated system, with rich ordered states and their properties are extremely sensitive to external stimuli. Recently, the experimental observation of spin-orbit coupling induced Mott insulator in Sr2IrO4 as well as the theoretical prediction of topological insulating state in other iridates, have attracted tremendous interest in the physics of strong correlation and spin-orbit coupling in 4d/5d compounds. We observe an itinerant ferromagnetic ground state of Ca2RuO4 film in stark contrast to the Mott-insulating state in bulk Ca2RuO4. We have also established the epitaxial strain effect on the transport and magnetic properties for the (Ca,Sr)2RuO4 thin films. For Sr2IrO4 thin films, we will show that the Jeff = 1/2 moment orientation can be modulated by epitaxial strain. In addition, we discovered novel Ba7Ir3O13+x thin films which exhibit colossal permittivity. / acase@tulane.edu

Contemporary learning theories derive much of their explanatory power from the assumption that all stimuli presented vie for associative strength, the assumption of Shared Weight Space (SWS). Theories based on this assumption have proven successful in explaining many of the observed conditioning phenomena in animals. However, work with humans has proven more complex due to outside knowledge, biases, and heuristics (see, e.g., Chapman, 1991; Msetfi et al., 2005; Perales et al., 2004; Tversky & Kahneman, 1974; Viken et al., 2005; Waldmann, 2000 & 2001). The present series of experiments sought to test the assumption of SWS in a task that is less susceptible to the influence of "top-down" factors. An information processing task (i.e., the correlated flankers task) was used so that human participants were completing a central task (i.e., responding to the target) and were unaware as to the importance of the contingencies in the designs, yet were still exposed to them via the irrelevant information (i.e., flankers). Four compound conditioning phenomena were studied in order to test the assumption of SWS. Evidence for the simple predictions coming from SWS theories was mixed. However, a slightly more complex version of these theories can explain the entire pattern of data quite elegantly.

The vast and complex wealth of information available to researchers often leads to a systematic review, which involves a detailed and comprehensive plan and search strategy with the goal of identifying, appraising, and synthesizing all relevant studies on a particular topic. A meta–analysis, conducted ideally as part of a comprehensive systematic review, statistically synthesizes evidence from multiple independent studies to produce one overall conclusion. The increasingly widespread use of meta–analysis has led to growing interest in meta–analytic methods for rare events and sparse data. Conventional approaches tend to perform very poorly in such settings. Recent work in this area has provided options for sparse data, but these are still often hampered when heterogeneity across the available studies differs based on treatment group. Heterogeneity arises when participants in a study are more correlated than participants across studies, often stemming from differences in the administration of the treatment, study design, or measurement of the outcome. We propose several new exact methods that accommodate this common contingency, providing more reliable statistical tests when such patterns on heterogeneity are observed. First, we develop a permutation–based approach that can also be used as a basis for computing exact confidence intervals when estimating the effect size. Second, we extend the permutation–based approach to the network meta–analysis setting. Third, we develop a new exact confidence distribution approach for effect size estimation. We show these new methods perform markedly better than traditional methods when events are rare, and heterogeneity is present.