Thesis advisor: Pankaj Mehta / Thesis advisor: Ziqiang Wang / Nature has revealed an astounding degree of phylogenetic and physiological diversity in natural environments -- especially in the microbial world. Microbial communities are incredibly diverse, ranging from 500-1000 species in human guts to over 1000 species in marine ecosystems. Historically, theoretical ecologists have devoted considerable effort to analyzing ecosystems consisting of a few species. However, analytical approaches and theoretical insights derived from small ecosystems consisting of a few species may not scale up to diverse ecosystems. Understanding such large complex ecosystems poses fundamental challenges to current theories and analytical approaches for modeling and understanding the microbial world. One promising approach for tackling this challenge that I develop in my thesis is to adapt and expand ideas from statistical mechanics to theoretical ecology. Statistical mechanics has helped us to understand how collective behaviors emerge from the interaction of many individual components. In this thesis, I present a unified theoretical framework for understanding complex ecosystems based on statistical mechanics, random matrix theories, and convex optimization. My thesis work has three key aspects: modeling, simulations, and theories. Modeling: Classical ecological models often focus on predator-prey relationships. However, this is not the norm in the microbial world. Unlike most macroscopic organisms, microbes relie on consuming and producing small organic molecules for energy and reproduction. In this thesis, we develop a new Microbial Consumer Resource Model that takes into account these types of metabolic cross-feeding interactions. We demonstrate that this model can qualitatively reproduce and explain statistical patterns observed in large survey data, including Earth Microbiome Project and the Human Microbiome Project. Simulations: Computational simulations are essential in theoretical ecology. Complex ecological models often involve ordinary differential equations (ODE) containing hundreds to thousands of interacting variables. Typical ODE solvers are based on numerical integration methods, which are both time and resource intensive. To overcome this bottleneck, we derived a surprising duality between constrained convex optimization and generalized consumer-resource models describing ecological dynamics. This allows us to develop a fast algorithm to solve the steady-state of complex ecological models. This improves computational performance by between 2-3 orders of magnitude compared to direct numerical integration of the corresponding ODEs. Theories:Few theoretical approaches allow for the analytic study of communities containing a large number of species. Recently, there has been considerable interest in the idea that ecosystems can be thought of as a type of disordered systems. This mapping suggests that understanding community coexistence patterns is actually a problem in "spin-glass'' physics. This has motivated physicists to use insights from spin glass theory to uncover the universal features of complex ecosystems. In this thesis, I use and extend the cavity method, originally developed in spin glass theories, to answer fundamental ecological questions regarding the stability, diversity, and robustness of ecosystems. I use the cavity method to derive new species backing bounds and uncover novel phase transitions to typicality. / Thesis (PhD) — Boston College, 2021. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Physics.

Dá-se o nome de sistemas de reputação a mecanismos em que membros de uma comunidade emitem avaliações sobre os demais e a partir destas se inferem quais dos membros podem ou não ser considerados confiáveis. Apresentamos, nesta dissertação de mestrado, um estudo sobre estes sistemas. Modela-se o problema de calcular reputações a partir de avaliações não-confiáveis como um problema de inferência estatística, que é então analisado com o uso de uma técnica conhecida como propagação de crenças, permitindo que obtenhamos estimativas. Em seguida, utilizamo-nos da relação existente entre problemas de inferência e mecânica estatística para realizar um estudo analítico mais profundo, por meio de uma generalização do método de cavidade. São traçados diagramas de fase, em que se observam regiões de parâmetros para as quais o problema torna-se mais difícil de resolver; esta análise nos dá alguma intuição sobre o problema, possibilitando que sejam propostas melhorias aos métodos existentes para tratá-lo. / It\'s given the name of reputation system to mechanisms in which members of a community issue each other ratings and from these it is inferred which can be trusted and which can\'t. We present, in this master\'s dissertation, a study on these systems. The problem of calculating reputations from unreliable ratings is modeled as one of statistical inference, and then analyzed with the use of a technique known as belief propagation, allowing us to obtain estimatives. Next, we use the existing relation between inference problems and statistical mechanics to motivate a deeper study, by means of a generalization of the cavity method. Phase diagrams are drawn, making possible to identify regions of parameters for which the problem is harder to solve; this analysis brings insight to the problem, allowing one to propose improvements to the methods available for it\'s treatment.

Dá-se o nome de sistemas de reputação a mecanismos em que membros de uma comunidade emitem avaliações sobre os demais e a partir destas se inferem quais dos membros podem ou não ser considerados confiáveis. Apresentamos, nesta dissertação de mestrado, um estudo sobre estes sistemas. Modela-se o problema de calcular reputações a partir de avaliações não-confiáveis como um problema de inferência estatística, que é então analisado com o uso de uma técnica conhecida como propagação de crenças, permitindo que obtenhamos estimativas. Em seguida, utilizamo-nos da relação existente entre problemas de inferência e mecânica estatística para realizar um estudo analítico mais profundo, por meio de uma generalização do método de cavidade. São traçados diagramas de fase, em que se observam regiões de parâmetros para as quais o problema torna-se mais difícil de resolver; esta análise nos dá alguma intuição sobre o problema, possibilitando que sejam propostas melhorias aos métodos existentes para tratá-lo. / It\'s given the name of reputation system to mechanisms in which members of a community issue each other ratings and from these it is inferred which can be trusted and which can\'t. We present, in this master\'s dissertation, a study on these systems. The problem of calculating reputations from unreliable ratings is modeled as one of statistical inference, and then analyzed with the use of a technique known as belief propagation, allowing us to obtain estimatives. Next, we use the existing relation between inference problems and statistical mechanics to motivate a deeper study, by means of a generalization of the cavity method. Phase diagrams are drawn, making possible to identify regions of parameters for which the problem is harder to solve; this analysis brings insight to the problem, allowing one to propose improvements to the methods available for it\'s treatment.

Complex networks are an important class of models used to describe the behaviour of a very broad category of systems which appear in different fields of science ranging from physics, biology and statistics to computer science and other disciplines. This set of models includes spin systems on a graph, neural networks, decision networks, spreading disease, financial trade, social networks and all systems which can be represented as interacting agents on some sort of graph architecture. In this thesis, by using the theoretical framework of statistical mechanics, the equilibrium and the dynamical behaviour of such systems is studied. For the equilibrium case, after presenting the region graph free energy approximation, the Survey Propagation method, previously used to investi- gate the low temperature phase of complex systems on tree-like topologies, is extended to the case of loopy graph architectures. For time-dependent behaviour, both discrete-time and continuous-time dynamics are considered. It is shown how to extend the cavity method ap- proach from a tool used to study equilibrium properties of complex systems to the discrete-time dynamical scenario. A closure scheme of the dynamic message-passing equation based on a Markovian approximations is presented. This allows to estimate non-equilibrium marginals of spin models on a graph with reversible dynamics. As an alternative to this approach, an extension of region graph variational free energy approximations to the non-equilibrium case is also presented. Non-equilibrium functionals that, when minimized with constraints, lead to approximate equations for out-of-equilibrium marginals of general spin models are introduced and discussed. For the continuous-time dynamics a novel approach that extends the cav- ity method also to this case is discussed. The main result of this part is a Cavity Master Equation which, together with an approximate version of the Master Equation, constitutes a closure scheme to estimate non-equilibrium marginals of continuous-time spin models. The investigation of dynamics of spin systems is concluded by applying a quasi-equilibrium approach to a sim- ple case. A way to test self-consistently the assumptions of the method as well as its limits is discussed. In the final part of the thesis, analogies and differences between the graph- ical model approaches discussed in the manuscript and causal analysis in statistics are presented. / <p>QC 20160904</p>

Dans les dernières années, plusieurs problèmes ont été étudiés à l'interface entre la physique statistique et l'informatique. La raison étant que, souvent, ces problèmes peuvent être réinterprétés dans le langage de la physique des systèmes désordonnés, où un grand nombre de variables interagit à travers champs locales qui dépendent de l'état du quartier environnant. Parmi les nombreuses applications de l'optimisation combinatoire le routage optimal sur les réseaux de communication est l'objet de la première partie de la thèse. Nous allons exploiter la méthode de la cavité pour formuler des algorithmes efficaces de type ‘’message-passing’’ et donc résoudre plusieurs variantes du problème grâce à sa mise en œuvre numérique. Dans un deuxième temps, nous allons décrire un modèle pour approcher la version dynamique de la méthode de la cavité, ce qui permet de diminuer la complexité du problème de l'exponentielle de polynôme dans le temps. Ceci sera obtenu en utilisant le formalisme de ‘’Matrix Product State’’ de la mécanique quantique.Un autre sujet qui a suscité beaucoup d'intérêt en physique statistique de processus dynamiques est la marche aléatoire sur les réseaux. La théorie a été développée depuis de nombreuses années dans le cas que la topologie dessous est un réseau de dimension d. Au contraire le cas des réseaux aléatoires a été abordé que dans la dernière décennie, laissant de nombreuses questions encore ouvertes pour obtenir des réponses. Démêler plusieurs aspects de ce thème fera l'objet de la deuxième partie de la thèse. En particulier, nous allons étudier le nombre moyen de sites distincts visités au cours d'une marche aléatoire et caractériser son comportement en fonction de la topologie du graphe. Enfin, nous allons aborder les événements rares statistiques associées aux marches aléatoires sur les réseaux en utilisant le ‘’Large deviations formalism’’. Deux types de transitions de phase dynamiques vont se poser à partir de simulations numériques. Nous allons conclure décrivant les principaux résultats d'une œuvre indépendante développée dans le cadre de la physique hors de l'équilibre. Un système résoluble en deux particules browniens entouré par un bain thermique sera étudiée fournissant des détails sur une interaction à médiation par du bain résultant de la présence du bain. / In the last years several problems been studied at the interface between statistical physics and computer science. The reason being that often these problems can be reinterpreted in the language of physics of disordered systems, where a big number of variables interacts through local fields dependent on the state of the surrounding neighborhood. Among the numerous applications of combinatorial optimisation the optimal routing on communication networks is the subject of the first part of the thesis. We will exploit the cavity method to formulate efficient algorithms of type message-passing and thus solve several variants of the problem through its numerical implementation. At a second stage, we will describe a model to approximate the dynamic version of the cavity method, which allows to decrease the complexity of the problem from exponential to polynomial in time. This will be obtained by using the Matrix Product State formalism of quantum mechanics. Another topic that has attracted much interest in statistical physics of dynamic processes is the random walk on networks. The theory has been developed since many years in the case the underneath topology is a d-dimensional lattice. On the contrary the case of random networks has been tackled only in the past decade, leaving many questions still open for answers. Unravelling several aspects of this topic will be the subject of the second part of the thesis. In particular we will study the average number of distinct sites visited during a random walk and characterize its behaviour as a function of the graph topology. Finally, we will address the rare events statistics associated to random walks on networks by using the large-deviations formalism. Two types of dynamic phase transitions will arise from numerical simulations, unveiling important aspects of these problems. We will conclude outlining the main results of an independent work developed in the context of out-of-equilibrium physics. A solvable system made of two Brownian particles surrounded by a thermal bath will be studied providing details about a bath-mediated interaction arising for the presence of the bath.

We study how to perform effective Bayesian inference in high-dimensional sparse Factor Analysis models with a zero-norm, sparsity-inducing prior on the model parameters. Such priors represent a methodological ideal, but Bayesian inference in such models is usually regarded as impractical. We test this view. After empirically characterising the properties of existing algorithmic approaches, we use techniques from statistical mechanics to derive a theory of optimal learning in the restricted setting of sparse PCA with a single factor. Finally, we describe a novel `Dense Message Passing' algorithm (DMP) which achieves near-optimal performance on synthetic data generated from this model.DMP exploits properties of high-dimensional problems to operate successfully on a densely connected graphical model. Similar algorithms have been developed in the statistical physics community and previously applied to inference problems in coding and sparse classification. We demonstrate that DMP out-performs both a newly proposed variational hybrid algorithm and two other recently published algorithms (SPCA and emPCA) on synthetic data while it explains at least the same amount of variance, for a given level of sparsity, in two gene expression datasets used in previous studies of sparse PCA.A significant potential advantage of DMP is that it provides an estimate of the marginal likelihood which can be used for hyperparameter optimisation. We show that, for the single factor case, this estimate exhibits good qualitative agreement both with theoretical predictions and with the hyperparameter posterior inferred by a collapsed Gibbs sampler. Preliminary work on an extension to inference of multiple factors indicates its potential for selecting an optimal model from amongst candidates which differ both in numbers of factors and their levels of sparsity.

Durant cette thèse, je me suis intéressé à deux thématiques générales qui peuvent être explorées dans des systèmes d’atomes froids : d’une part, la dynamique hors-équilibre d’un système quantique isolé, et d’autre part l’influence du désordre sur un système fortement corrélé à basse température. Dans un premier temps, nous avons développé une méthode de champ moyen, qui permet de résoudre la dynamique unitaire dans un modèle à géométrie particulière, le réseau complètement connecté. Cette approche permet d’établir une correspondance entre la dynamique unitaire du système quantique et des équations du mouvement classique. Nous avons mis à profit cette méthode pour étudier le phénomène de transition dynamique qui se signale, dans des modèles de champ moyen, par une singularité des observables aux temps longs, en fonction des paramètres initiaux ou finaux de la trempe. Nous avons montré l’existence d’une transition dynamique quantique dans les modèle de Bose-Hubbard, d’Ising en champ transverse et le modèle de Jaynes-Cummings. Ces résultats confirment l’existence d’un lien fort entre la présence d’une transition de phase quantique et d’une transition dynamique.Dans un second temps, nous avons étudié un modèle de théorie des champs relativiste avec symétrie O(N) afin de comprendre l’influence des fluctuations sur ces singularités. À l’ordre dominant en grand N, nous avons montré que la transition dynamique s’apparente à un phénomène critique. En effet, à la transition dynamique, les fonctions de corrélations suivent une loi d’échelle à temps égaux et à temps arbitraires. Il existe également une longueur caractéristique qui diverge à l’approche du point de transition. D’autre part, il apparaît que le point fixe admet une interprétation en terme de particules sans masse se propageant librement. Enfin, nous avons montré que la dynamique asymptotique au niveau du point fixe s’apparente à celle d’une trempe d’un état symétrique dans la phase de symétrie brisée. Le troisième volet de cette thèse apporte des éléments nouveaux pour la compréhension du diagramme des phases du modèle de Bose-Hubbard en présence de désordre. Pour ce faire,nous avons utilisé et étendu la méthode de la cavité quantique en champ moyen de Ioffe et Mézard, qui doit être utilisée avec la méthode des répliques. De cette manière, il est possible d’obtenir des résultats analytiques pour les exposants des lois de probabilité de la susceptibilité.Nos résultats indiquent que dans les différents régimes de la transition de phase de superfluide vers isolant, les lois d’échelle conventionnelles sont tantôt applicables, tantôt remplacées par une loi d’activation. Enfin, les exposants critiques varient continûment à la transition conventionnelle. / The fast progress of cold atoms experiments in the last decade has allowed to explore new aspects of strongly correlated systems. This thesis deals with two such general themes: the out of equilibrium dynamics of closed quantum systems, and the impact of disorder on strongly correlated bosons at zero temperature. Among the different questions about out of equilibrium dynamics, the phenomenon of dynamical transition is still lacking a complete understanding. The transition is typically signalled, in mean-field, by a singular behaviour of observables as a function of the parameters of the quench. In this thesis, a mean field method is developed to give evidence of a strong link between the quantum phase transition at zero temperature and the dynamical transition. We then study using field theory techniques a relativistic O($N$) model, and show that the dynamical transition bears similarities with a critical phenomenon. In this context, the dynamical transition also appears to be formally related to the dynamics of symmetry breaking. The second part of this thesis is about the disordered Bose-Hubbard model and the nature of its phase transitions. We use and extend the cavity mean field method, introduced by Ioffe and Mezard to obtain analytical results from the quantum cavity method and the replica trick. We find that the conventional transition, with power law scaling, is changed into an activated scaling in some regions of the phase diagram. Furthermore, the critical exponents are continuously varying along the conventional transition. These intriguing properties call for further investigations using different methods.

Un grand nombre des problèmes d'optimisation, ainsi que des problèmes inverses, combinatoires ou hors équilibre qui apparaissent en physique statistique des systèmes complexes, peuvent être représentés comme un ensemble des variables en interaction sur un certain réseau. Bien que la recette universelle pour traiter ces problèmes n'existe pas, la compréhension qualitative et quantitative des problèmes complexes sur des graphes a fait des grands progrès au cours de ces dernières années. Un rôle particulier a été joué par des concepts empruntés de la physique des verres de spin et la théorie des champs, qui ont eu beaucoup de succès en ce qui concerne la description des propriétés statistiques des systèmes complexes et le développement d'algorithmes efficaces pour des problèmes concrets.En première partie de cette thèse, nous étudions des problèmes de diffusion sur des réseaux, avec la dynamique hors équilibre. En utilisant la méthode de cavité sur des trajectoires dans le temps, nous montrons comment dériver des équations dynamiques dites "message-passing'' pour une large classe de modèles avec une dynamique unidirectionnelle -- la propriété clef qui permet de résoudre le problème. Ces équations sont asymptotiquement exactes pour des graphes localement en arbre et en général représentent une bonne approximation pour des réseaux réels. Nous illustrons cette approche avec une application des équations dynamiques pour résoudre le problème inverse d'inférence de la source d'épidémie dans le modèle "susceptible-infected-recovered''.Dans la seconde partie du manuscrit, nous considérons un problème d'optimisation d'appariement planaire optimal sur une ligne. En exploitant des techniques de la théorie de champs et des arguments combinatoires, nous caractérisons une transition de phase topologique qui se produit dans un modèle désordonné simple, le modèle de Bernoulli. Visant une application à la physique des structures secondaires de l'ARN, nous discutons la relation entre la transition d'appariement parfait-imparfait et la transition de basse température connue entre les états fondu et vitreux de biopolymère; nous proposons également des modèles généralisés qui suggèrent une correspondance exacte entre la matrice des contacts et la séquence des nucléotides, permettant ainsi de donner un sens à la notion des alphabets effectifs non-entiers. / A large number of optimization, inverse, combinatorial and out-of-equilibrium problems, arising in the statistical physics of complex systems, allow for a convenient representation in terms of disordered interacting variables defined on a certain network. Although a universal recipe for dealing with these problems does not exist, the recent years have seen a serious progress in understanding and quantifying an important number of hard problems on graphs. A particular role has been played by the concepts borrowed from the physics of spin glasses and field theory, that appeared to be extremely successful in the description of the statistical properties of complex systems and in the development of efficient algorithms for concrete problems.In the first part of the thesis, we study the out-of-equilibrium spreading problems on networks. Using dynamic cavity method on time trajectories, we show how to derive dynamic message-passing equations for a large class of models with unidirectional dynamics -- the key property that makes the problem solvable. These equations are asymptotically exact for locally tree-like graphs and generally provide a good approximation for real-world networks. We illustrate the approach by applying the dynamic message-passing equations for susceptible-infected-recovered model to the inverse problem of inference of epidemic origin. In the second part of the manuscript, we address the optimization problem of finding optimal planar matching configurations on a line. Making use of field-theory techniques and combinatorial arguments, we characterize a topological phase transition that occurs in the simple Bernoulli model of disordered matching. As an application to the physics of the RNA secondary structures, we discuss the relation of the perfect-imperfect matching transition to the known molten-glass transition at low temperatures, and suggest generalized models that incorporate a one-to-one correspondence between the contact matrix and the nucleotide sequence, thus giving sense to the notion of effective non-integer alphabets.

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