831 resultados para distributed conditional computation


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Realistic rendering animation is known to be an expensive processing task when physically-based global illumination methods are used in order to improve illumination details. This paper presents an acceleration technique to compute animations in radiosity environments. The technique is based on an interpolated approach that exploits temporal coherence in radiosity. A fast global Monte Carlo pre-processing step is introduced to the whole computation of the animated sequence to select important frames. These are fully computed and used as a base for the interpolation of all the sequence. The approach is completely view-independent. Once the illumination is computed, it can be visualized by any animated camera. Results present significant high speed-ups showing that the technique could be an interesting alternative to deterministic methods for computing non-interactive radiosity animations for moderately complex scenarios

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Path integration is known to provide information to keep track of spatial location. Surprisingly, few investigations concerning sex differences in computation of the traveling distance have been done. This work was aimed at analyzing the reproduction of both passive and active linear displacements in women and men. To this end, the displacement of blindfolded subjects was done in a wheelchair, then on foot, three times in each condition for a fixed distance. Copies of passive and active traveling distance, distance estimations and pointing responses towards the starting point were analyzed. In passive condition and comparatively to men, women error was larger. Whereas traveling distance was generally underestimated in women, it was overestimated in men. In active condition, no sex differences were observed. When blindfolded subjects have to estimate the traveling distance, the female error was larger than the male one. But, when subjects were asked to indicate the visual cue corresponding to the traveling distance, the male error was larger than the female one. Finally, pointing to the starting point (0°) after a whole-body rotation showed a larger deviation from 0° in men than in women. These results suggest that sex of the subjects influence brain computation of path integration information.

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This research is aimed to find a solution for a distributed storage system adapted for CoDeS. By studying how DSSs work and how they are implemented, we can conclude how we can implement a DSS compatible with CoDeS requirements.

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Deduction allows us to draw consequences from previous knowledge. Deductive reasoning can be applied to several types of problem, for example, conditional, syllogistic, and relational. It has been assumed that the same cognitive operations underlie solutions to them all; however, this hypothesis remains to be tested empirically. We used event-related fMRI, in the same group of subjects, to compare reasoning-related activity associated with conditional and syllogistic deductive problems. Furthermore, we assessed reasoning-related activity for the two main stages of deduction, namely encoding of premises and their integration. Encoding syllogistic premises for reasoning was associated with activation of BA 44/45 more than encoding them for literal recall. During integration, left fronto-lateral cortex (BA 44/45, 6) and basal ganglia activated with both conditional and syllogistic reasoning. Besides that, integration of syllogistic problems additionally was associated with activation of left parietal (BA 7) and left ventro-lateral frontal cortex (BA 47). This difference suggests a dissociation between conditional and syllogistic reasoning at the integration stage. Our finding indicates that the integration of conditional and syllogistic reasoning is carried out by means of different, but partly overlapping, sets of anatomical regions and by inference, cognitive processes. The involvement of BA 44/45 during both encoding (syllogisms) and premise integration (syllogisms and conditionals) suggests a central role in deductive reasoning for syntactic manipulations and formal/linguistic representations.

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The Computational Biophysics Group at the Universitat Pompeu Fabra (GRIB-UPF) hosts two unique computational resources dedicated to the execution of large scale molecular dynamics (MD) simulations: (a) the ACMD molecular-dynamics software, used on standard personal computers with graphical processing units (GPUs); and (b) the GPUGRID. net computing network, supported by users distributed worldwide that volunteer GPUs for biomedical research. We leveraged these resources and developed studies, protocols and open-source software to elucidate energetics and pathways of a number of biomolecular systems, with a special focus on flexible proteins with many degrees of freedom. First, we characterized ion permeation through the bactericidal model protein Gramicidin A conducting one of the largest studies to date with the steered MD biasing methodology. Next, we addressed an open problem in structural biology, the determination of drug-protein association kinetics; we reconstructed the binding free energy, association, and dissaciociation rates of a drug like model system through a spatial decomposition and a Makov-chain analysis. The work was published in the Proceedings of the National Academy of Sciences and become one of the few landmark papers elucidating a ligand-binding pathway. Furthermore, we investigated the unstructured Kinase Inducible Domain (KID), a 28-peptide central to signalling and transcriptional response; the kinetics of this challenging system was modelled with a Markovian approach in collaboration with Frank Noe’s group at the Freie University of Berlin. The impact of the funding includes three peer-reviewed publication on high-impact journals; three more papers under review; four MD analysis components, released as open-source software; MD protocols; didactic material, and code for the hosting group.

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In a distributed key distribution scheme, a set of servers helps a set of users in a group to securely obtain a common key. Security means that an adversary who corrupts some servers and some users has no information about the key of a noncorrupted group. In this work, we formalize the security analysis of one such scheme which was not considered in the original proposal. We prove the scheme is secure in the random oracle model, assuming that the Decisional Diffie-Hellman (DDH) problem is hard to solve. We also detail a possible modification of that scheme and the one in which allows us to prove the security of the schemes without assuming that a specific hash function behaves as a random oracle. As usual, this improvement in the security of the schemes is at the cost of an efficiency loss.

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Error-correcting codes and matroids have been widely used in the study of ordinary secret sharing schemes. In this paper, the connections between codes, matroids, and a special class of secret sharing schemes, namely, multiplicative linear secret sharing schemes (LSSSs), are studied. Such schemes are known to enable multiparty computation protocols secure against general (nonthreshold) adversaries.Two open problems related to the complexity of multiplicative LSSSs are considered in this paper. The first one deals with strongly multiplicative LSSSs. As opposed to the case of multiplicative LSSSs, it is not known whether there is an efficient method to transform an LSSS into a strongly multiplicative LSSS for the same access structure with a polynomial increase of the complexity. A property of strongly multiplicative LSSSs that could be useful in solving this problem is proved. Namely, using a suitable generalization of the well-known Berlekamp–Welch decoder, it is shown that all strongly multiplicative LSSSs enable efficient reconstruction of a shared secret in the presence of malicious faults. The second one is to characterize the access structures of ideal multiplicative LSSSs. Specifically, the considered open problem is to determine whether all self-dual vector space access structures are in this situation. By the aforementioned connection, this in fact constitutes an open problem about matroid theory, since it can be restated in terms of representability of identically self-dual matroids by self-dual codes. A new concept is introduced, the flat-partition, that provides a useful classification of identically self-dual matroids. Uniform identically self-dual matroids, which are known to be representable by self-dual codes, form one of the classes. It is proved that this property also holds for the family of matroids that, in a natural way, is the next class in the above classification: the identically self-dual bipartite matroids.

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Aim  Recently developed parametric methods in historical biogeography allow researchers to integrate temporal and palaeogeographical information into the reconstruction of biogeographical scenarios, thus overcoming a known bias of parsimony-based approaches. Here, we compare a parametric method, dispersal-extinction-cladogenesis (DEC), against a parsimony-based method, dispersal-vicariance analysis (DIVA), which does not incorporate branch lengths but accounts for phylogenetic uncertainty through a Bayesian empirical approach (Bayes-DIVA). We analyse the benefits and limitations of each method using the cosmopolitan plant family Sapindaceae as a case study.Location  World-wide.Methods  Phylogenetic relationships were estimated by Bayesian inference on a large dataset representing generic diversity within Sapindaceae. Lineage divergence times were estimated by penalized likelihood over a sample of trees from the posterior distribution of the phylogeny to account for dating uncertainty in biogeographical reconstructions. We compared biogeographical scenarios between Bayes-DIVA and two different DEC models: one with no geological constraints and another that employed a stratified palaeogeographical model in which dispersal rates were scaled according to area connectivity across four time slices, reflecting the changing continental configuration over the last 110 million years.Results  Despite differences in the underlying biogeographical model, Bayes-DIVA and DEC inferred similar biogeographical scenarios. The main differences were: (1) in the timing of dispersal events - which in Bayes-DIVA sometimes conflicts with palaeogeographical information, and (2) in the lower frequency of terminal dispersal events inferred by DEC. Uncertainty in divergence time estimations influenced both the inference of ancestral ranges and the decisiveness with which an area can be assigned to a node.Main conclusions  By considering lineage divergence times, the DEC method gives more accurate reconstructions that are in agreement with palaeogeographical evidence. In contrast, Bayes-DIVA showed the highest decisiveness in unequivocally reconstructing ancestral ranges, probably reflecting its ability to integrate phylogenetic uncertainty. Care should be taken in defining the palaeogeographical model in DEC because of the possibility of overestimating the frequency of extinction events, or of inferring ancestral ranges that are outside the extant species ranges, owing to dispersal constraints enforced by the model. The wide-spanning spatial and temporal model proposed here could prove useful for testing large-scale biogeographical patterns in plants.

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En aquest projecte he avaluat un seguit de plataformes per veure quina era la millor pertal d’integrar les eines que proporcionen serveis del projecte TENCompetence.Per començar el projecte plantejaré el context del projecte. Com se situa al marc del projecte TENCompetence on he desenvolupat aquest treball fi de carrera. Tot seguit es veuen quines eines disposem per tal d’accedir als diferents serveis que ens proporciona el projecte.Comento els escenaris on s’aplicarà la tecnologia que triem i finalment comento les diferents plataformes web on integrarem les diferents eines.A continuació he realitzat un capítol per tal de comentar l’anàlisi de requeriments del’escenari d’aplicació de cada pilot. Per a cada escenari aplico unes determinades eines a un determinat context, i per tant hi han unes necessitats concretes que he de recollir. Per plasmar-ho en paper he realitzat l’anàlisi de requeriments. Un cop recollides totes les dades he pogut feruna selecció de la plataforma contenidora que més s’escau a cada pilot.Amb els requeriments i la plataforma seleccionada, he realitzat un disseny per a cada pilot. Després de refinar el disseny he realitzat la implementació per tal de cobrir les necessitats dels pilots. També he aprofitat per veure quina tecnologia es pot utilitzar per tal d’integrar leseines dins de la plataforma.Amb la implementació feta he realitzat un seguit de proves per tal de veure els resultats aconseguits. Tot seguit he iniciat un procés iteractiu per tal refinar el disseny i millorar la implementació.

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The Baldwin effect can be observed if phenotypic learning influences the evolutionary fitness of individuals, which can in turn accelerate or decelerate evolutionary change. Evidence for both learning-induced acceleration and deceleration can be found in the literature. Although the results for both outcomes were supported by specific mathematical or simulation models, no general predictions have been achieved so far. Here we propose a general framework to predict whether evolution benefits from learning or not. It is formulated in terms of the gain function, which quantifies the proportional change of fitness due to learning depending on the genotype value. With an inductive proof we show that a positive gain-function derivative implies that learning accelerates evolution, and a negative one implies deceleration under the condition that the population is distributed on a monotonic part of the fitness landscape. We show that the gain-function framework explains the results of several specific simulation models. We also use the gain-function framework to shed some light on the results of a recent biological experiment with fruit flies.

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Abstract Sitting between your past and your future doesn't mean you are in the present. Dakota Skye Complex systems science is an interdisciplinary field grouping under the same umbrella dynamical phenomena from social, natural or mathematical sciences. The emergence of a higher order organization or behavior, transcending that expected of the linear addition of the parts, is a key factor shared by all these systems. Most complex systems can be modeled as networks that represent the interactions amongst the system's components. In addition to the actual nature of the part's interactions, the intrinsic topological structure of underlying network is believed to play a crucial role in the remarkable emergent behaviors exhibited by the systems. Moreover, the topology is also a key a factor to explain the extraordinary flexibility and resilience to perturbations when applied to transmission and diffusion phenomena. In this work, we study the effect of different network structures on the performance and on the fault tolerance of systems in two different contexts. In the first part, we study cellular automata, which are a simple paradigm for distributed computation. Cellular automata are made of basic Boolean computational units, the cells; relying on simple rules and information from- the surrounding cells to perform a global task. The limited visibility of the cells can be modeled as a network, where interactions amongst cells are governed by an underlying structure, usually a regular one. In order to increase the performance of cellular automata, we chose to change its topology. We applied computational principles inspired by Darwinian evolution, called evolutionary algorithms, to alter the system's topological structure starting from either a regular or a random one. The outcome is remarkable, as the resulting topologies find themselves sharing properties of both regular and random network, and display similitudes Watts-Strogtz's small-world network found in social systems. Moreover, the performance and tolerance to probabilistic faults of our small-world like cellular automata surpasses that of regular ones. In the second part, we use the context of biological genetic regulatory networks and, in particular, Kauffman's random Boolean networks model. In some ways, this model is close to cellular automata, although is not expected to perform any task. Instead, it simulates the time-evolution of genetic regulation within living organisms under strict conditions. The original model, though very attractive by it's simplicity, suffered from important shortcomings unveiled by the recent advances in genetics and biology. We propose to use these new discoveries to improve the original model. Firstly, we have used artificial topologies believed to be closer to that of gene regulatory networks. We have also studied actual biological organisms, and used parts of their genetic regulatory networks in our models. Secondly, we have addressed the improbable full synchronicity of the event taking place on. Boolean networks and proposed a more biologically plausible cascading scheme. Finally, we tackled the actual Boolean functions of the model, i.e. the specifics of how genes activate according to the activity of upstream genes, and presented a new update function that takes into account the actual promoting and repressing effects of one gene on another. Our improved models demonstrate the expected, biologically sound, behavior of previous GRN model, yet with superior resistance to perturbations. We believe they are one step closer to the biological reality.

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We analyze how unemployment, job finding and job separation rates react to neutral and investment-specific technology shocks. Neutral shocks increase unemployment and explain a substantial portion of unemployment volatility; investment-specific shocks expand employment and hours worked and mostly contribute to hours worked volatility. Movements in the job separation rates are responsible for the impact response of unemployment while job finding rates for movements along its adjustment path. Our evidence qualifies the conclusions by Hall (2005) and Shimer (2007) and warns against using search models with exogenous separation rates to analyze the effects of technology shocks.

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The integrity of the cornea, the most anterior part of the eye, is indispensable for vision. Forty-five million individuals worldwide are bilaterally blind and another 135 million have severely impaired vision in both eyes because of loss of corneal transparency; treatments range from local medications to corneal transplants, and more recently to stem cell therapy. The corneal epithelium is a squamous epithelium that is constantly renewing, with a vertical turnover of 7 to 14 days in many mammals. Identification of slow cycling cells (label-retaining cells) in the limbus of the mouse has led to the notion that the limbus is the niche for the stem cells responsible for the long-term renewal of the cornea; hence, the corneal epithelium is supposedly renewed by cells generated at and migrating from the limbus, in marked opposition to other squamous epithelia in which each resident stem cell has in charge a limited area of epithelium. Here we show that the corneal epithelium of the mouse can be serially transplanted, is self-maintained and contains oligopotent stem cells with the capacity to generate goblet cells if provided with a conjunctival environment. Furthermore, the entire ocular surface of the pig, including the cornea, contains oligopotent stem cells (holoclones) with the capacity to generate individual colonies of corneal and conjunctival cells. Therefore, the limbus is not the only niche for corneal stem cells and corneal renewal is not different from other squamous epithelia. We propose a model that unifies our observations with the literature and explains why the limbal region is enriched in stem cells.