FEM Updating of the Heritage Court Building Structure

This paper describes results of a model updating study conducted on a 15-storey reinforced concrete shear core building. The output-only modal identification results obtained from ambient vibration measurements of the building were used to update a finite element model of the structure. The starting model of the structure was developed from the information provided in the design documentation of the building. Different parameters of the model were then modified using an automated procedure to improve the correlation between measured and calculated modal parameters. Careful attention was placed to the selection of the parameters to be modified by the updating software in order to ensure that the necessary changes to the model were realistic and physically realisable and meaningfull. The paper highlights the model updating process and provides an assessment of the usefulness of using an automatic model updating procedure combined with results from an output-only modal identification

Efficient Nondomination Level Update Method for Steady-State Evolutionary Multiobjective Optimization

Nondominated sorting (NDS), which divides a population into several nondomination levels (NDLs), is a basic step in many evolutionary multiobjective optimization (EMO) algorithms. It has been widely studied in a generational evolution model, where the environmental selection is performed after generating a whole population of offspring. However, in a steady-state evolution model, where a population is updated right after the generation of a new candidate, the NDS can be extremely time consuming. This is especially severe when the number of objectives and population size become large. In this paper, we propose an efficient NDL update method to reduce the cost for maintaining the NDL structure in steady-state EMO. Instead of performing the NDS from scratch, our method only updates the NDLs of a limited number of solutions by extracting the knowledge from the current NDL structure. Notice that our NDL update method is performed twice at each iteration. One is after the reproduction, the other is after the environmental selection. Extensive experiments fully demonstrate that, comparing to the other five state-of-the-art NDS methods, our proposed method avoids a significant amount of unnecessary comparisons, not only in the synthetic data sets, but also in some real optimization scenarios. Last but not least, we find that our proposed method is also useful for the generational evolution model

Federated Learning Using Variance Reduced Stochastic Gradient for Probabilistically Activated Agents

This paper proposes an algorithm for Federated Learning (FL) with a two-layer structure that achieves both variance reduction and a faster convergence rate to an optimal solution in the setting where each agent has an arbitrary probability of selection in each iteration. In distributed machine learning, when privacy matters, FL is a functional tool. Placing FL in an environment where it has some irregular connections of agents (devices), reaching a trained model in both an economical and quick way can be a demanding job. The first layer of our algorithm corresponds to the model parameter propagation across agents done by the server. In the second layer, each agent does its local update with a stochastic and variance-reduced technique called Stochastic Variance Reduced Gradient (SVRG). We leverage the concept of variance reduction from stochastic optimization when the agents want to do their local update step to reduce the variance caused by stochastic gradient descent (SGD). We provide a convergence bound for our algorithm which improves the rate from $O(\frac{1}{\sqrt{K}})$ to $O(\frac{1}{K})$ by using a constant step-size. We demonstrate the performance of our algorithm using numerical examples

Adaptive dynamics with interaction structure

Evolutionary dynamics depend critically on a population's interaction structure - the pattern of which individuals interact with which others, depending on the state of the population and the environment. Previous research has shown, for example, that cooperative behaviors disfavored in well-mixed populations can be favored when interactions occur only between spatial neighbors or group members. Combining the adaptive dynamics approach with recent advances in evolutionary game theory, we here introduce a general mathematical framework for analyzing the long-term evolution of continuous game strategies for a broad class of evolutionary models, encompassing many varieties of interaction structure. Our main result, the "canonical equation of adaptive dynamics with interaction structure", characterizes expected evolutionary trajectories resulting from any such model, thereby generalizing a central tool of adaptive dynamics theory. Interestingly, the effects of different interaction structures and update rules on evolutionary trajectories are fully captured by just two real numbers associated with each model, which are independent of the considered game. The first, a structure coefficient, quantifies the effects on selection pressures, and thus on the shapes of expected evolutionary trajectories. The second, an effective population size, quantifies the effects on selection responses, and thus on the expected rates of adaptation. Applying our results to two social dilemmas, we show how the range of evolutionarily stable cooperative behaviors systematically varies with a model's structure coefficient

Constraining dark sector perturbations II: ISW and CMB lensing tomography

Any Dark Energy (DE) or Modified Gravity (MG) model that deviates from a cosmological constant requires a consistent treatment of its perturbations, which can be described in terms of an effective entropy perturbation and an anisotropic stress. We have considered a recently proposed generic parameterisation of DE/MG perturbations and compared it to data from the Planck satellite and six galaxy catalogues, including temperature-galaxy (Tg), CMB lensing-galaxy and galaxy-galaxy (gg) correlations. Combining these observables of structure formation with tests of the background expansion allows us to investigate the properties of DE/MG both at the background and the perturbative level. Our constraints on DE/MG are mostly in agreement with the cosmological constant paradigm, while we also find that the constraint on the equation of state w (assumed to be constant) depends on the model assumed for the perturbation evolution. We obtain $w=-0.92^{+0.20}_{-0.16}$ (95% CL; CMB+gg+Tg) in the entropy perturbation scenario; in the anisotropic stress case the result is $w=-0.86^{+0.17}_{-0.16}$. Including the lensing correlations shifts the results towards higher values of w. If we include a prior on the expansion history from recent Baryon Acoustic Oscillations (BAO) measurements, we find that the constraints tighten closely around $w=-1$, making it impossible to measure any DE/MG perturbation evolution parameters. If, however, upcoming observations from surveys like DES, Euclid or LSST show indications for a deviation from a cosmological constant, our formalism will be a useful tool towards model selection in the dark sector.Comment: 25 pages, 8 figures; minor update for consistency with version accepted by JCAP (13/01/2015

PIGSPro: prediction of immunoGlobulin structures v2

PIGSpro is a significant upgrade of the popular PIGS server for the prediction of the structure of immunoglobulins. The software has been completely rewritten in python following a similar pipeline as in the original method, but including, at various steps, relevant modifications found to improve its prediction accuracy, as demonstrated here. The steps of the pipeline include the selection of the appropriate framework for predicting the conserved regions of the molecule by homology; the target template alignment for this portion of the molecule; the selection of the main chain conformation of the hypervariable loops according to the canonical structure model, the prediction of the third loop of the heavy chain (H3) for which complete canonical structures are not available and the packing of the light and heavy chain if derived from different templates. Each of these steps has been improved including updated methods developed along the years. Last but not least, the user interface has been completely redesigned and an automatic monthly update of the underlying database has been implemented. The method is available as a web server at http://biocomputing.it/pigspro

Adaptive input selection and evolving neural fuzzy networks modeling

This paper suggests an evolving approach to develop neural fuzzy networks for system modeling. The approach uses an incremental learning procedure to simultaneously select the model inputs, to choose the neural network structure, and to update the network weights. Candidate models with larger and smaller number of input variables than the current model are constructed and tested concurrently. The procedure employs a statistical test in each learning step to choose the best model amongst the current and candidate models. Membership functions can be added or deleted to adjust input space granulation and the neural network structure. Granulation and structure adaptation depend of the modeling error. The weights of the neural networks are updated using a gradient-descent algorithm with optimal learning rate. Prediction and nonlinear system identification examples illustrate the usefulness of the approach. Comparisons with state of the art evolving fuzzy modeling alternatives are performed to evaluate performance from the point of view of modeling error. Simulation results show that the evolving adaptive input selection modeling neural network approach achieves as high as, or higher performance than the remaining evolving modeling methods81314CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE MINAS GERAIS - FAPEMIG305906/2014-3não temnão te

Neural Evidence for Adaptive Strategy Selection in Value-Based Decision-Making

In everyday life, humans often encounter complex environments in which multiple sources of information can influence their decisions. We propose that in such situations, people select and apply different strategies representing different cognitive models of the decision problem. Learning advances by evaluating the success of using a strategy and eventually by switching between strategies. To test our strategy selection model, we investigated how humans solve a dynamic learning task with complex auditory and visual information, and assessed the underlying neural mechanisms with functional magnetic resonance imaging. Using the model, we were able to capture participants' choices and to successfully attribute expected values and reward prediction errors to activations in the dopaminoceptive system (e.g., ventral striatum [VS]) as well as decision conflict to signals in the anterior cingulate cortex. The model outperformed an alternative approach that did not update decision strategies, but the relevance of information itself. Activation of sensory areas depended on whether the selected strategy made use of the respective source of information. Selection of a strategy also determined how value-related information influenced effective connectivity between sensory systems and the VS. Our results suggest that humans can structure their search for and use of relevant information by adaptively selecting between decision strategie

Updating Complex Value Databeses

Query languages and their optimizations have been a very important issue in the database community. Languages for updating databases, however, have not been studied to the same extent, although they are clearly important since databases must change over time. The structure and expressiveness of updates is largely dependent on the data model. In relational databases, for example, the update language typically allows the user to specify changes to individual fields of a subset of a relation that meets some selection criterion. The syntax is terse, specifying only the pieces of the database that are to be altered. Because of its simplicity, most of the optimizations take place in the internal processing of the update rather than at the language level. In complex value databases, the need for a terse and optimizable update language is much greater, due to the deeply nested structures involved. Starting with a query language for complex value databases called the Collection Programming Language (CPL), we describe an extension called CPL+ which provides a convenient and intuitive specification of updates on complex values. CPL is a functional language, with powerful optimizations achieved through rewrite rules. Additional rewrite rules are derived for CPL+ and a notion of deltafication is introduced to transform complete updates, expressed as conventional CPL expressions, into equivalent update expressions in CPL+. As a result of applying these transformations, the performance of complex updates can increase substantially