The Network Company in Catalonia : ICT, Productivity, Competitiveness, Salaries and Performance in Catalonia's Companies. Research report (synthesis document)

Resum en anglès del projecte de recerca L'empresa xarxa a Catalunya. TIC, productivitat, competitivitat, salaris i beneficis a l'empresa catalana té com a objectiu principal constatar que la consolidació d'un nou model estratègic, organitzatiu i d'activitat empresarial, vinculat amb la inversió i l'ús de les TIC (o empresa xarxa), modifica substancialment els patrons de comportament dels resultats empresarials, en especial la productivitat, la competitivitat, les retribucions dels treballadors i el benefici. La contrastació empírica de les hipòtesis de treball l'hem feta per mitjà de les dades d'una enquesta a una mostra representativa de 2.038 empreses catalanes. Amb la perspectiva de l'impacte de la inversió i l'ús de les TIC no s'aprecia una relació directa entre els processos d'innovació digital i els resultats de l'activitat de l'empresa catalana. En aquest sentit, hem hagut de segmentar el teixit productiu català per a buscar les organitzacions en què el procés de coinnovació tecnològica digital i organitzativa és més present i en què la intensitat de l'ús del coneixement és un recurs molt freqüent per a poder copsar impactes rellevants en els principals resultats empresarials. Això és així perquè l'economia catalana, avui, presenta una estructura productiva dual.

Key competences in the new ventures: a model for evaluating

This research studies from an internal view based on the Competency-Based Perspective (CBP), key organizational competencies developed for small new business. CBP is chosen in an attempt to explain the differences characterizing the closed companies from the consolidated ones. The main contribution of this paper is the definition of a set of key organizational competencies for new ventures from services and low technology based sectors. Using the classification proposed by [1] and a review of the entrepreneurship literature, the main competencies were defined and classified as: managerial, input-based, transformation-based, and output-based competencies. The proposed model for evaluating new ventures organizational competence is tested by means of Structural Equation

Availability analysis of GMPLS connections based on physical network topology

This paper presents a study of connection availability in GMPLS over optical transport networks (OTN) taking into account different network topologies. Two basic path protection schemes are considered and compared with the no protection case. The selected topologies are heterogeneous in geographic coverage, network diameter, link lengths, and average node degree. Connection availability is also computed considering the reliability data of physical components and a well-known network availability model. Results show several correspondences between suitable path protection algorithms and several network topology characteristics

University and Network Society : research report (synthesis document)

Document de síntesi d'aquest estudi que analitza -seguint una metodologia quantitativa basada en una mostra representativa de 2.093 professors i 23.864 estudiants i reforçada amb elements qualitatius- la transició que es produeix en el sistema universitari públic català cap a un model més adaptat a les noves necessitats de la societat xarxa. Per a això, es posa especial èmfasi en l'anàlisi dels usos que es fa d'Internet (l'eina clau de la societat xarxa) en el món universitari i en les transformacions que es donen o es donaran com a conseqüència d'aquests usos.

Classificació de sons percussius amb un model generatiu

El treball que presentem a continuació desenvolupa un marc teòric i pràctic per a l'avaluació i estudi d'un model generatiu aplicat a tasques discriminatives de senyals sonores sense component harmònica. El model generatiu està basat en la construcció de l'anomenada deep belief network, un tipus de xarxa neuronal generativa que permet realitzar tasques de classificació i regressió com també de reconstrucció dels seus estats interns.A partir de l'anàlisi realitzada hem pogut obtenir resultats en classificació aparellats amb els resultats de l'estat de l'art de classificadors de sons inharmònics. Tot i no establir una clara superioritat envers altres mètodes, el present treball ha permés desenvolupar una anàlisi per almodel avaluat amb moltes possibilitats de millora en un futur per altres treballs. Al llarg del treball es demostra la seva eficàcia en tasques discriminatives, com també la capacitat de reduir la dimensionalitat de les dades d'entrada al model i les possibilitats de reconstruir els seus estats interns per a obtenir unes sortides de dades de la xarxa similars a les entrades de descriptors.El desenvolupament centrat en la deep belief network ens ha permés construir un entorn unificat d'avaluació de diferents mètodes d'aprenentatge, construcció i adequació de diferents descriptors sonors i una posterior visualització d'estats interns del mateix, que han possibilitat una avaluaciócomparativa i unificada respecte altres mètodes classificadors de l'estat de l'art. També ens ha permés desenvolupar una implementació en un llenguatge d'alt nivell, que ha reportat més significància per a l'enteniment i anàlisi del model avaluat, amb una argumentació més sòlida.Els resultats i l'anàlisi que reportem són significatius i positius per al model avaluat, i degut a la poca literatura existent en el camp de classificació de sons inharmònics com els sons percussius,creiem que és una aportació interessant i significativa per al camp en el que s'engloba el treball.

A randomized concave programming method for choice network revenue management

Models incorporating more realistic models of customer behavior, as customers choosing froman offer set, have recently become popular in assortment optimization and revenue management.The dynamic program for these models is intractable and approximated by a deterministiclinear program called the CDLP which has an exponential number of columns. However, whenthe segment consideration sets overlap, the CDLP is difficult to solve. Column generationhas been proposed but finding an entering column has been shown to be NP-hard. In thispaper we propose a new approach called SDCP to solving CDLP based on segments and theirconsideration sets. SDCP is a relaxation of CDLP and hence forms a looser upper bound onthe dynamic program but coincides with CDLP for the case of non-overlapping segments. Ifthe number of elements in a consideration set for a segment is not very large (SDCP) can beapplied to any discrete-choice model of consumer behavior. We tighten the SDCP bound by(i) simulations, called the randomized concave programming (RCP) method, and (ii) by addingcuts to a recent compact formulation of the problem for a latent multinomial-choice model ofdemand (SBLP+). This latter approach turns out to be very effective, essentially obtainingCDLP value, and excellent revenue performance in simulations, even for overlapping segments.By formulating the problem as a separation problem, we give insight into why CDLP is easyfor the MNL with non-overlapping considerations sets and why generalizations of MNL posedifficulties. We perform numerical simulations to determine the revenue performance of all themethods on reference data sets in the literature.

A new compact linear programming formulation for choice network revenue management

The choice network revenue management model incorporates customer purchase behavioras a function of the offered products, and is the appropriate model for airline and hotel networkrevenue management, dynamic sales of bundles, and dynamic assortment optimization.The optimization problem is a stochastic dynamic program and is intractable. A certainty-equivalencerelaxation of the dynamic program, called the choice deterministic linear program(CDLP) is usually used to generate dyamic controls. Recently, a compact linear programmingformulation of this linear program was given for the multi-segment multinomial-logit (MNL)model of customer choice with non-overlapping consideration sets. Our objective is to obtaina tighter bound than this formulation while retaining the appealing properties of a compactlinear programming representation. To this end, it is natural to consider the affine relaxationof the dynamic program. We first show that the affine relaxation is NP-complete even for asingle-segment MNL model. Nevertheless, by analyzing the affine relaxation we derive a newcompact linear program that approximates the dynamic programming value function betterthan CDLP, provably between the CDLP value and the affine relaxation, and often comingclose to the latter in our numerical experiments. When the segment consideration sets overlap,we show that some strong equalities called product cuts developed for the CDLP remain validfor our new formulation. Finally we perform extensive numerical comparisons on the variousbounds to evaluate their performance.

Aggregate fluctuations and the network structure of intersectoral trade

This paper analyzes the flow of intermediate inputs across sectors by adopting a network perspective on sectoral interactions. I apply these tools to show how fluctuationsin aggregate economic activity can be obtained from independent shocks to individualsectors. First, I characterize the network structure of input trade in the U.S. On thedemand side, a typical sector relies on a small number of key inputs and sectors arehomogeneous in this respect. However, in their role as input-suppliers sectors do differ:many specialized input suppliers coexist alongside general purpose sectors functioningas hubs to the economy. I then develop a model of intersectoral linkages that can reproduce these connectivity features. In a standard multisector setup, I use this modelto provide analytical expressions linking aggregate volatility to the network structureof input trade. I show that the presence of sectoral hubs - by coupling productiondecisions across sectors - leads to fluctuations in aggregates.

The P-median problem in a changing network: The case of Barcelona

In this paper a p--median--like model is formulated to address theissue of locating new facilities when there is uncertainty. Severalpossible future scenarios with respect to demand and/or the travel times/distanceparameters are presented. The planner will want a strategy of positioning thatwill do as ``well as possible'' over the future scenarios. This paper presents a discrete location model formulation to address this P--Medianproblem under uncertainty. The model is applied to the location of firestations in Barcelona.

Assessing an intuitive condition for stability under a range of traffic conditions via a generalised Lu-Kumar network

We argue the importance both of developing simple sufficientconditions for the stability of general multiclass queueing networks and also of assessing such conditions under a range of assumptions on the weight of the traffic flowing between service stations. To achieve the former, we review a peak-rate stability condition and extend its range of application and for the latter, we introduce a generalisation of the Lu-Kumar network on which the stability condition may be tested for a range of traffic configurations. The peak-rate condition is close to exact when the between-station traffic is light, but degrades as this traffic increases.

On bounds for network revenue management

The Network Revenue Management problem can be formulated as a stochastic dynamic programming problem (DP or the\optimal" solution V *) whose exact solution is computationally intractable. Consequently, a number of heuristics have been proposed in the literature, the most popular of which are the deterministic linear programming (DLP) model, and a simulation based method, the randomized linear programming (RLP) model. Both methods give upper bounds on the optimal solution value (DLP and PHLP respectively). These bounds are used to provide control values that can be used in practice to make accept/deny decisions for booking requests. Recently Adelman [1] and Topaloglu [18] have proposed alternate upper bounds, the affine relaxation (AR) bound and the Lagrangian relaxation (LR) bound respectively, and showed that their bounds are tighter than the DLP bound. Tight bounds are of great interest as it appears from empirical studies and practical experience that models that give tighter bounds also lead to better controls (better in the sense that they lead to more revenue). In this paper we give tightened versions of three bounds, calling themsAR (strong Affine Relaxation), sLR (strong Lagrangian Relaxation) and sPHLP (strong Perfect Hindsight LP), and show relations between them. Speciffically, we show that the sPHLP bound is tighter than sLR bound and sAR bound is tighter than the LR bound. The techniques for deriving the sLR and sPHLP bounds can potentially be applied to other instances of weakly-coupled dynamic programming.

Validation procedures in radiological diagnostic models. Neural network and logistic regression

The objective of this paper is to compare the performance of twopredictive radiological models, logistic regression (LR) and neural network (NN), with five different resampling methods. One hundred and sixty-seven patients with proven calvarial lesions as the only known disease were enrolled. Clinical and CT data were used for LR and NN models. Both models were developed with cross validation, leave-one-out and three different bootstrap algorithms. The final results of each model were compared with error rate and the area under receiver operating characteristic curves (Az). The neural network obtained statistically higher Az than LR with cross validation. The remaining resampling validation methods did not reveal statistically significant differences between LR and NN rules. The neural network classifier performs better than the one based on logistic regression. This advantage is well detected by three-fold cross-validation, but remains unnoticed when leave-one-out or bootstrap algorithms are used.

Generalized Multiobjective Multitree model solution using MOEA

In a previous paper a novel Generalized Multiobjective Multitree model (GMM-model) was proposed. This model considers for the first time multitree-multicast load balancing with splitting in a multiobjective context, whose mathematical solution is a whole Pareto optimal set that can include several results than it has been possible to find in the publications surveyed. To solve the GMM-model, in this paper a multi-objective evolutionary algorithm (MOEA) inspired by the Strength Pareto Evolutionary Algorithm (SPEA) is proposed. Experimental results considering up to 11 different objectives are presented for the well-known NSF network, with two simultaneous data flows

A limited area model intercomparison on the "Montserrat-2000" flash-flood event using statistical and deterministic methods

In the scope of the European project Hydroptimet, INTERREG IIIB-MEDOCC programme, limited area model (LAM) intercomparison of intense events that produced many damages to people and territory is performed. As the comparison is limited to single case studies, the work is not meant to provide a measure of the different models' skill, but to identify the key model factors useful to give a good forecast on such a kind of meteorological phenomena. This work focuses on the Spanish flash-flood event, also known as "Montserrat-2000" event. The study is performed using forecast data from seven operational LAMs, placed at partners' disposal via the Hydroptimet ftp site, and observed data from Catalonia rain gauge network. To improve the event analysis, satellite rainfall estimates have been also considered. For statistical evaluation of quantitative precipitation forecasts (QPFs), several non-parametric skill scores based on contingency tables have been used. Furthermore, for each model run it has been possible to identify Catalonia regions affected by misses and false alarms using contingency table elements. Moreover, the standard "eyeball" analysis of forecast and observed precipitation fields has been supported by the use of a state-of-the-art diagnostic method, the contiguous rain area (CRA) analysis. This method allows to quantify the spatial shift forecast error and to identify the error sources that affected each model forecasts. High-resolution modelling and domain size seem to have a key role for providing a skillful forecast. Further work is needed to support this statement, including verification using a wider observational data set.

Model-Free Reconstruction of Excitatory Neuronal Connectivity from Calcium Imaging Signals

A systematic assessment of global neural network connectivity through direct electrophysiological assays has remained technically infeasible, even in simpler systems like dissociated neuronal cultures. We introduce an improved algorithmic approach based on Transfer Entropy to reconstruct structural connectivity from network activity monitored through calcium imaging. We focus in this study on the inference of excitatory synaptic links. Based on information theory, our method requires no prior assumptions on the statistics of neuronal firing and neuronal connections. The performance of our algorithm is benchmarked on surrogate time series of calcium fluorescence generated by the simulated dynamics of a network with known ground-truth topology. We find that the functional network topology revealed by Transfer Entropy depends qualitatively on the time-dependent dynamic state of the network (bursting or non-bursting). Thus by conditioning with respect to the global mean activity, we improve the performance of our method. This allows us to focus the analysis to specific dynamical regimes of the network in which the inferred functional connectivity is shaped by monosynaptic excitatory connections, rather than by collective synchrony. Our method can discriminate between actual causal influences between neurons and spurious non-causal correlations due to light scattering artifacts, which inherently affect the quality of fluorescence imaging. Compared to other reconstruction strategies such as cross-correlation or Granger Causality methods, our method based on improved Transfer Entropy is remarkably more accurate. In particular, it provides a good estimation of the excitatory network clustering coefficient, allowing for discrimination between weakly and strongly clustered topologies. Finally, we demonstrate the applicability of our method to analyses of real recordings of in vitro disinhibited cortical cultures where we suggest that excitatory connections are characterized by an elevated level of clustering compared to a random graph (although not extreme) and can be markedly non-local.