Modeling Adoption and Usage of Competing Products

The emergence and wide-spread use of online social networks has led to a dramatic increase on the availability of social activity data. Importantly, this data can be exploited to investigate, at a microscopic level, some of the problems that have captured the attention of economists, marketers and sociologists for decades, such as, e.g., product adoption, usage and competition. In this paper, we propose a continuous-time probabilistic model, based on temporal point processes, for the adoption and frequency of use of competing products, where the frequency of use of one product can be modulated by those of others. This model allows us to efficiently simulate the adoption and recurrent usages of competing products, and generate traces in which we can easily recognize the effect of social influence, recency and competition. We then develop an inference method to efficiently fit the model parameters by solving a convex program. The problem decouples into a collection of smaller subproblems, thus scaling easily to networks with hundred of thousands of nodes. We validate our model over synthetic and real diffusion data gathered from Twitter, and show that the proposed model does not only provides a good fit to the data and more accurate predictions than alternatives but also provides interpretable model parameters, which allow us to gain insights into some of the factors driving product adoption and frequency of use

Learning and Forecasting Opinion Dynamics in Social Networks

Social media and social networking sites have become a global pinboard for exposition and discussion of news, topics, and ideas, where social media users often update their opinions about a particular topic by learning from the opinions shared by their friends. In this context, can we learn a data-driven model of opinion dynamics that is able to accurately forecast opinions from users? In this paper, we introduce SLANT, a probabilistic modeling framework of opinion dynamics, which represents users opinions over time by means of marked jump diffusion stochastic differential equations, and allows for efficient model simulation and parameter estimation from historical fine grained event data. We then leverage our framework to derive a set of efficient predictive formulas for opinion forecasting and identify conditions under which opinions converge to a steady state. Experiments on data gathered from Twitter show that our model provides a good fit to the data and our formulas achieve more accurate forecasting than alternatives

Shaping Social Activity by Incentivizing Users

Events in an online social network can be categorized roughly into endogenous events, where users just respond to the actions of their neighbors within the network, or exogenous events, where users take actions due to drives external to the network. How much external drive should be provided to each user, such that the network activity can be steered towards a target state? In this paper, we model social events using multivariate Hawkes processes, which can capture both endogenous and exogenous event intensities, and derive a time dependent linear relation between the intensity of exogenous events and the overall network activity. Exploiting this connection, we develop a convex optimization framework for determining the required level of external drive in order for the network to reach a desired activity level. We experimented with event data gathered from Twitter, and show that our method can steer the activity of the network more accurately than alternatives

Mala relación médico paciente asociado al incumplimiento de farmacoterapia hipoglucemiante oral en diabéticos. Hospital Distrital Santa Isabel- El Porvenir- Trujillo 2022

Este proyecto. de. tesis tiene como objetivo. determinar. sí una mala relación médico paciente se relaciona con el incumplimiento de la farmacoterapia hipoglicemiante oral en diabéticos en el Hospital Distrital Santa Isabel- El Porvenir Trujillo 2022; con respecto a la metodología se trata de un estudio observacional, analítico de casos y controles. Conformado por la población de pacientes con diagnóstico de diabetes mellitus tipo 2 atendidos en el Hospital Distrital Santa Isabel en el periodo. de octubre a noviembre del 2022. Con respecto a la población. de estudio. se estima una muestra, constituida en el grupo de casos por 72 pacientes con incumplimiento al tratamiento oral con hipoglucemiantes y en el grupo control: por 144 pacientes que cumplen con el tratamiento. Se estimará la prueba Chi cuadrado para verificar si la diferencia de los porcentajes de. mala. relación médico paciente entre el grupo de casos y controles es o no significativa; se considerará significancia estadística cuando el valor de p sea menor a 0.05, se determinará el odds ratio puntual e interválico, se realizará el análisis de regresión logística multivariante para las variables intervinientes.Tesis de segunda especialida

Pangenome Evolution in theMarine Bacterium Alteromonas

Wehave examined a collection of the free-livingmarine bacterium Alteromonas genomeswith cores diverging in average nucleotide identities ranging from 99.98% to 73.35%, i.e., frommicrobes that can be consideredmembers of a natural clone (like in a clinical epidemiological outbreak) to borderline genus level. The genomes were largely syntenic allowing a precise delimitation of the core and flexible regions in each. The core was 1.4Mb (ca. 30% of the typical strain genome size). Recombination rates along the core were high among strains belonging to the same species (37.7–83.7% of all nucleotide polymorphisms) but they decreased sharply between species (18.9–5.1%). Regarding the flexible genome, itsmain expansion occurred within the boundaries of the species, i.e., strains of the same species already have a large and diverse flexible genome. Flexible regions occupy mostly fixed genomic locations. Four large genomic islands are involved in the synthesis of strain-specific glycosydic receptors that we have called glycotypes. These genomic regions are exchanged by homologous recombination within and between species and there is evidence for their import from distant taxonomic units (other genera within the family). In addition, several hotspots for integration of gene cassettes by illegitimate recombination are distributed throughout the genome. They code for features that give each clone specific properties to interact with their ecological niche andmustflowfast throughout thewholegenus as they are found, withnearly identical sequences, in different species. Models for the generation of this genomic diversity involving phage predation are discussed.This work was funded by the Spanish MINECO (Grants BFPU2013-48007-P)This work was funded by the Generalitat Valenciana PROMETEO (Grants II/2014/012

Modeling the Dynamics of Online Learning Activity

People are increasingly relying on the Web and social media to find solutions to their problems in a wide range of domains. In this online setting, closely related problems often lead to the same characteristic learning pattern, in which people sharing these problems visit related pieces of information, perform almost identical queries or, more generally, take a series of similar actions. In this paper, we introduce a novel modeling framework for clustering continuous-time grouped streaming data, the hierarchical Dirichlet Hawkes process (HDHP), which allows us to automatically uncover a wide variety of learning patterns from detailed traces of learning activity. Our model allows for efficient inference, scaling to millions of actions taken by thousands of users. Experiments on real data gathered from Stack Overflow reveal that our framework can recover meaningful learning patterns in terms of both content and temporal dynamics, as well as accurately track users' interests and goals over time

Enhancing the Accuracy and Fairness of Human Decision Making

Societies often rely on human experts to take a wide variety of decisions affecting their members, from jail-or-release decisions taken by judges and stop-and-frisk decisions taken by police officers to accept-or-reject decisions taken by academics. In this context, each decision is taken by an expert who is typically chosen uniformly at random from a pool of experts. However, these decisions may be imperfect due to limited experience, implicit biases, or faulty probabilistic reasoning. Can we improve the accuracy and fairness of the overall decision making process by optimizing the assignment between experts and decisions? In this paper, we address the above problem from the perspective of sequential decision making and show that, for different fairness notions from the literature, it reduces to a sequence of (constrained) weighted bipartite matchings, which can be solved efficiently using algorithms with approximation guarantees. Moreover, these algorithms also benefit from posterior sampling to actively trade off exploitation---selecting expert assignments which lead to accurate and fair decisions---and exploration---selecting expert assignments to learn about the experts' preferences and biases. We demonstrate the effectiveness of our algorithms on both synthetic and real-world data and show that they can significantly improve both the accuracy and fairness of the decisions taken by pools of experts

Modeling the Dynamics of Online Learning Activity

People are increasingly relying on the Web and social media to find solutions to their problems in a wide range of domains. In this online setting, closely related problems often lead to the same characteristic learning pattern, in which people sharing these problems visit related pieces of information, perform almost identical queries or, more generally, take a series of similar actions. In this paper, we introduce a novel modeling framework for clustering continuous-time grouped streaming data, the hierarchical Dirichlet Hawkes process (HDHP), which allows us to automatically uncover a wide variety of learning patterns from detailed traces of learning activity. Our model allows for efficient inference, scaling to millions of actions taken by thousands of users. Experiments on real data gathered from Stack Overflow reveal that our framework can recover meaningful learning patterns in terms of both content and temporal dynamics, as well as accurately track users' interests and goals over time

Genomes of Planktonic Acidimicrobiales: Widening Horizons for Marine Actinobacteria by Metagenomics

The genomes of four novel marine Actinobacteria have been assembled from large metagenomic data sets derived from the Mediterranean deep chlorophyll maximum (DCM). These are the first marine representatives belonging to the order Acidimicrobiales and only the second group of planktonic marine Actinobacteria to be described. Their streamlined genomes and photoheterotrophic lifestyle suggest that they are planktonic, free-living microbes. A novel rhodopsin clade, acidirhodopsins, related to freshwater actinorhodopsins, was found in these organisms. Their genomes suggest a capacity to assimilate C2 compounds, some using the glyoxylate bypass and others with the ethylmalonyl-coenzyme A (CoA) pathway. They are also able to derive energy from dimethylsulfopropionate (DMSP), sulfonate, and carbon monoxide oxidation, all commonly available in the marine habitat. These organisms appear to be prevalent in the deep photic zone at or around the DCM. The presence of sister clades to the marine Acidimicrobiales in freshwater aquatic habitats provides a new example of marine-freshwater transitions with potential evolutionary insights.This work was supported by projects MICROGEN (Programa CONSOLIDER-INGENIO 2010 CSD2009-00006) from the Spanish Ministerio de Ciencia e InnovaciónMEDIMAXBFPU2013-48007-P from the Spanish Ministerio de Economía y CompetitividadMaCuMBA311975 of the European Commission FP

Explaining microbial population genomics through phage predation

The remarkable diversity of genes within the pool of prokaryotic genomes belonging to the same species or pan-genome is difficult to reconcile with the widely accepted paradigm which asserts that periodic selection within bacterial populations would regularly purge genomic diversity by clonal replacement. Recent evidence from metagenomics indicates that even within a single sample a large diversity of genomes can be present for a single species. We have found that much of the differential gene content affects regions that are potential phage recognition targets. We therefore propose the operation of Constant-Diversity dynamics in which the diversity of prokaryotic populations is preserved by phage predation. We provide supporting evidence for this model from metagenomics, mathematical analysis and computer simulations. Periodic selection and phage predation dynamics are not mutually exclusive; we compare their predictions to indicate under which ecological circumstances each dynamics could predominate