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Search Results: 1 - 10 of 22710 matches for " Manuel Gomez-Rodriguez "
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Modeling Adoption and Usage of Competing Products
Isabel Valera,Manuel Gomez-Rodriguez
Computer Science , 2014,
Abstract: 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.
Inferring Networks of Diffusion and Influence
Manuel Gomez-Rodriguez,Jure Leskovec,Andreas Krause
Computer Science , 2010,
Abstract: Information diffusion and virus propagation are fundamental processes taking place in networks. While it is often possible to directly observe when nodes become infected with a virus or adopt the information, observing individual transmissions (i.e., who infects whom, or who influences whom) is typically very difficult. Furthermore, in many applications, the underlying network over which the diffusions and propagations spread is actually unobserved. We tackle these challenges by developing a method for tracing paths of diffusion and influence through networks and inferring the networks over which contagions propagate. Given the times when nodes adopt pieces of information or become infected, we identify the optimal network that best explains the observed infection times. Since the optimization problem is NP-hard to solve exactly, we develop an efficient approximation algorithm that scales to large datasets and finds provably near-optimal networks. We demonstrate the effectiveness of our approach by tracing information diffusion in a set of 170 million blogs and news articles over a one year period to infer how information flows through the online media space. We find that the diffusion network of news for the top 1,000 media sites and blogs tends to have a core-periphery structure with a small set of core media sites that diffuse information to the rest of the Web. These sites tend to have stable circles of influence with more general news media sites acting as connectors between them.
Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm
Hadi Daneshmand,Manuel Gomez-Rodriguez,Le Song,Bernhard Schoelkopf
Computer Science , 2014,
Abstract: Information spreads across social and technological networks, but often the network structures are hidden from us and we only observe the traces left by the diffusion processes, called cascades. Can we recover the hidden network structures from these observed cascades? What kind of cascades and how many cascades do we need? Are there some network structures which are more difficult than others to recover? Can we design efficient inference algorithms with provable guarantees? Despite the increasing availability of cascade data and methods for inferring networks from these data, a thorough theoretical understanding of the above questions remains largely unexplored in the literature. In this paper, we investigate the network structure inference problem for a general family of continuous-time diffusion models using an $l_1$-regularized likelihood maximization framework. We show that, as long as the cascade sampling process satisfies a natural incoherence condition, our framework can recover the correct network structure with high probability if we observe $O(d^3 \log N)$ cascades, where $d$ is the maximum number of parents of a node and $N$ is the total number of nodes. Moreover, we develop a simple and efficient soft-thresholding inference algorithm, which we use to illustrate the consequences of our theoretical results, and show that our framework outperforms other alternatives in practice.
Effect of Glucose Concentrations on the Growth and Metabolism of Brettanomyces bruxellensis under Aerobic Conditions  [PDF]
Ortiz-Mu?iz Benigno, Corro-Herrera Victor, Gomez-Rodriguez Javier, Domínguez-González José Manuel, Aguilar-Uscanga María Guadalupe
Advances in Microbiology (AiM) , 2013, DOI: 10.4236/aim.2013.33034

Acetic acid can be directly produced from glucose in one-step fermentation by using yeasts of the genus Brettanomyces bruxellensis, hence increasing the industrial application to manufacture products with simplified bioprocesses. Thereby, this work evaluates the influence of initial glucose concentration on the growth and acetic acid production by B. bruxellensis. The results obtained confirmed the presence of Crabtree effect on B. bruxellensis under low glucose concentrations. The maximum acetic acid concentration reached was 15.4 g·L-1 starting with 100 g·L-1 leading to a product yield of 0.154 g·g-1 and a specific acetic acid production rate of 0.05 g·g-1·h-1. The results also indicate that after reaching the acetic acid critic threshold of 4 g·L-1 the metabolism can induce the growth second phase even residual glucose was present on the culture media at high starting glucose concentrations. Additionally, it was observed a lineal relationship between cell viability and acetic acid production.

Back to the Past: Source Identification in Diffusion Networks from Partially Observed Cascades
Mehrdad Farajtabar,Manuel Gomez-Rodriguez,Nan Du,Mohammad Zamani,Hongyuan Zha,Le Song
Computer Science , 2015,
Abstract: When a piece of malicious information becomes rampant in an information diffusion network, can we identify the source node that originally introduced the piece into the network and infer the time when it initiated this? Being able to do so is critical for curtailing the spread of malicious information, and reducing the potential losses incurred. This is a very challenging problem since typically only incomplete traces are observed and we need to unroll the incomplete traces into the past in order to pinpoint the source. In this paper, we tackle this problem by developing a two-stage framework, which first learns a continuous-time diffusion network model based on historical diffusion traces and then identifies the source of an incomplete diffusion trace by maximizing the likelihood of the trace under the learned model. Experiments on both large synthetic and real-world data show that our framework can effectively go back to the past, and pinpoint the source node and its initiation time significantly more accurately than previous state-of-the-arts.
The missing atom as a source of carbon magnetism
M. M. Ugeda,I. Brihuega,F. Guinea,J. M. Gomez-Rodriguez
Physics , 2010, DOI: 10.1103/PhysRevLett.104.096804
Abstract: Atomic vacancies have a strong impact in the mechanical, electronic and magnetic properties of graphene-like materials. By artificially generating isolated vacancies on a graphite surface and measuring their local density of states on the atomic scale, we have shown how single vacancies modify the electronic properties of this graphene-like system. Our scanning tunneling microscopy experiments, complemented by tight binding calculations, reveal the presence of a sharp electronic resonance at the Fermi energy around each single graphite vacancy, which can be associated with the formation of local magnetic moments and implies a dramatic reduction of the charge carriers' mobility. While vacancies in single layer graphene naturally lead to magnetic couplings of arbitrary sign, our results show the possibility of inducing a macroscopic ferrimagnetic state in multilayered graphene samples just by randomly removing single C atoms.
A review of ureteral injuries after external trauma
Bruno MT Pereira, Michael P Ogilvie, Juan Gomez-Rodriguez, Mark L Ryan, Diego Pe?a, Antonio C Marttos, Louis R Pizano, Mark G McKenney
Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine , 2010, DOI: 10.1186/1757-7241-18-6
Abstract: Eighty-one articles pertaining to traumatic ureteral injuries were reviewed. Data from these studies were compiled and analyzed. The majority of the study population was young males. The proximal ureter was the most frequently injured portion. Associated injuries were present in 90.4% of patients. Admission urinalysis demonstrated hematuria in only 44.4% patients. Intravenous ureterogram (IVU) failed to diagnose ureteral injuries either upon admission or in the operating room in 42.8% of cases. Ureteroureterostomy, with or without indwelling stent, was the surgical procedure of choice for both trauma surgeons and urologists (59%). Complications occurred in 36.2% of cases. The mortality rate was 17%.The mechanism for ureteral injuries in adults is more commonly penetrating than blunt. The upper third of the ureter is more often injured than the middle and lower thirds. Associated injuries are frequently present. CT scan and retrograde pyelography accurately identify ureteral injuries when performed together. Ureteroureterostomy, with or without indwelling stent, is the surgical procedure of choice of both trauma surgeons and urologists alike. Delay in diagnosis is correlated with a poor prognosis.The proper management of a trauma victim is an increasingly relevant topic of discussion due to international warfare and the growing domestic incidence of traumatic injury. According to the Center for Disease Control and Prevention (CDC), trauma is the leading cause of death in children and young adults and overall is the fifth leading cause of death in the United States [1]. The World Health Organization classifies trauma as the 9th leading cause of death worldwide [2,3].Ureteral trauma was first reported in 1868 by Alfred Poland when he described the first case of disruption from blunt trauma [4]. The patient was a 33-year-old woman who died 6 days after being pinned between a platform and a railway carriage. At autopsy, in addition to many other injuries, the right urete
Neuro-Inspired Spike-Based Motion: From Dynamic Vision Sensor to Robot Motor Open-Loop Control through Spike-VITE
Fernando Perez-Pe?a,Arturo Morgado-Estevez,Alejandro Linares-Barranco,Angel Jimenez-Fernandez,Francisco Gomez-Rodriguez,Gabriel Jimenez-Moreno,Juan Lopez-Coronado
Sensors , 2013, DOI: 10.3390/s131115805
Abstract: In this paper we present a complete spike-based architecture: from a Dynamic Vision Sensor (retina) to a stereo head robotic platform. The aim of this research is to reproduce intended movements performed by humans taking into account as many features as possible from the biological point of view. This paper fills the gap between current spike silicon sensors and robotic actuators by applying a spike processing strategy to the data flows in real time. The architecture is divided into layers: the retina, visual information processing, the trajectory generator layer which uses a neuroinspired algorithm (SVITE) that can be replicated into as many times as DoF the robot has; and finally the actuation layer to supply the spikes to the robot (using PFM). All the layers do their tasks in a spike-processing mode, and they communicate each other through the neuro-inspired AER protocol. The open-loop controller is implemented on FPGA using AER interfaces developed by RTC Lab. Experimental results reveal the viability of this spike-based controller. Two main advantages are: low hardware resources (2% of a Xilinx Spartan 6) and power requirements (3.4 W) to control a robot with a high number of DoF (up to 100 for a Xilinx Spartan 6). It also evidences the suitable use of AER as a communication protocol between processing and actuation.
In situ observation of stress relaxation in epitaxial graphene
Alpha T. N'Diaye,Raoul van Gastel,Antonio J. Martinez-Galera,Johann Coraux,Hichem Hattab,Dirk Wall,Frank-J. Meyer zu Heringdorf,Michael Horn-von Hoegen,Jose M. Gomez-Rodriguez,Bene Poelsema,Carsten Busse,Thomas Michely
Physics , 2009, DOI: 10.1088/1367-2630/11/11/113056
Abstract: Upon cooling, branched line defects develop in epitaxial graphene grown at high temperature on Pt(111) and Ir(111). Using atomically resolved scanning tunneling microscopy we demonstrate that these defects are wrinkles in the graphene layer, i.e. stripes of partially delaminated graphene. With low energy electron microscopy (LEEM) we investigate the wrinkling phenomenon in situ. Upon temperature cycling we observe hysteresis in the appearance and disappearance of the wrinkles. Simultaneously with wrinkle formation a change in bright field imaging intensity of adjacent areas and a shift in the moire spot positions for micro diffraction of such areas takes place. The stress relieved by wrinkle formation results from the mismatch in thermal expansion coefficients of graphene and the substrate. A simple one-dimensional model taking into account the energies related to strain, delamination and bending of graphene is in qualitative agreement with our observations.
Submodular Inference of Diffusion Networks from Multiple Trees
Manuel Gomez Rodriguez,Bernhard Sch?lkopf
Computer Science , 2012,
Abstract: Diffusion and propagation of information, influence and diseases take place over increasingly larger networks. We observe when a node copies information, makes a decision or becomes infected but networks are often hidden or unobserved. Since networks are highly dynamic, changing and growing rapidly, we only observe a relatively small set of cascades before a network changes significantly. Scalable network inference based on a small cascade set is then necessary for understanding the rapidly evolving dynamics that govern diffusion. In this article, we develop a scalable approximation algorithm with provable near-optimal performance based on submodular maximization which achieves a high accuracy in such scenario, solving an open problem first introduced by Gomez-Rodriguez et al (2010). Experiments on synthetic and real diffusion data show that our algorithm in practice achieves an optimal trade-off between accuracy and running time.
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