Universities’ Performance in Knowledge Transfer: An Analysis of the Ibero-American Region Over the Golden Decade

Universities play a crucial role in the systems of innovation by transferring the results of R&D activities to society and industry. This contribution is even more important in the Ibero-American countries given that the other critical ‘player’ (i.e., the industry) exercises a less active role in the development of innovation compared to the OECD countries. The aim of this paper is to analyze the knowledge transfer activities of the Ibero-American Higher Education Systems over the period 2000-2010. Using that database by Barro (2015), this study provides an accurate diagnosis of the Ibero-American universities’ performance in knowledge transfer, suggesting a number of practical implications for university decision-makersS

Sleep and wakefulness in the cuneate nucleus: a computational study

We present a computational study about the influence of the sensorimotor cortex on the processing of the cuneate nucleus during sleep as well as wakefulness. Realistic computational models were developed supported by experimental data obtained from intact-brain preparations in cat. Furthermore, a physiologically plausible circuit is proposed and predictions under both different cortical stimulation and synaptic configurations are suggested. The computer simulations show that the CN circuitry (1) under sleep conditions can block the transmission of afferent sensory information, and (2) under awaking conditions can perform operations such as filtering and facilitation

A Computational Model of Cuneothalamic Projection Neurons

[Abstract] The dorsal column nuclei, cuneatus and gracilis, play a fundamental role in the processing and integration of somesthetic ascending information. Intracellular and patch-clamp recordings obtained in cat in vivo have shown that cuneothalamic projection neurons present two modes of activity: oscillatory and tonic (Canedo et al 1998 Neuroscience 84 603–17). The former is the basis of generating, in sleep and anaesthetized states, slow, delta and spindle rhythms under the control of the cerebral cortex (Mari˜no et al 2000 Neuroscience 95 657–73). The latter is needed, during wakefulness, to process somesthetic information in real time. To study this behaviour we have developed the first realistic computational model of the cuneothalamic projection neurons. The modelling was guided by experimental recordings, which suggest the existence of hyperpolarization-activated inward currents, transient low- and high-threshold calcium currents, and calcium-activated potassium currents. The neuronal responses were simulated during (1) sleep, (2) transition from sleep to wakefulness and (3) wakefulness under both excitatory and inhibitory synaptic input. In wakefulness the model predicts a set of synaptically driven firing modes that could be associated with information processing strategies in the middle cuneate nucleus

A realistic computational model of the local circuitry of the cuneate nucleus

Intracellular recordings obtained under cutaneous and lemniscal stimulation show that the afferent fibers can establish excitatory and inhibitory synaptic connections with cuneothalamic neurons [5]. In addition, distinct types of recurrent collaterals with the capability of either exciting or inhibiting both cuneothalamic neurons and interneurons were also discovered [6]. With these data we have generated hypothesis about which circuits are implicated and also developed realistic computational models to test the hypothesis and study the cuneate properties [17,18]. The results show that the cuneate could perform spatial and temporal filtering and therefore detect dynamic edges

Do we need hundreds of classifiers to solve real world classification problems?

We evaluate 179 classifiers arising from 17 families (discriminant analysis, Bayesian, neural networks, support vector machines, decision trees, rule-based classifiers, boosting, bagging, stacking, random forests and other ensembles, generalized linear models, nearest-neighbors, partial least squares and principal component regression, logistic and multinomial regression, multiple adaptive regression splines and other methods), implemented in Weka, R (with and without the caret package), C and Matlab, including all the relevant classifiers available today. We use 121 data sets, which represent the whole UCI data base (excluding the large- scale problems) and other own real problems, in order to achieve significant conclusions about the classifier behavior, not dependent on the data set collection. The classifiers most likely to be the bests are the random forest (RF) versions, the best of which (implemented in R and accessed via caret) achieves 94.1% of the maximum accuracy overcoming 90% in the 84.3% of the data sets. However, the difference is not statistically significant with the second best, the SVM with Gaussian kernel implemented in C using LibSVM, which achieves 92.3% of the maximum accuracy. A few models are clearly better than the remaining ones: random forest, SVM with Gaussian and polynomial kernels, extreme learning machine with Gaussian kernel, C5.0 and avNNet (a committee of multi-layer perceptrons implemented in R with the caret package). The random forest is clearly the best family of classifiers (3 out of 5 bests classifiers are RF), followed by SVM (4 classifiers in the top-10), neural networks and boosting ensembles (5 and 3 members in the top-20, respectively)We would like to acknowledge support from the Spanish Ministry of Science and Innovation (MICINN), which supported this work under projects TIN2011-22935 and TIN2012-32262S

Cortical modulation of dorsal column nuclei: a computational study

[Abstract] We present a computational study aimed at exploring the sensorimotor cortex modulation of the behaviour of dorsal column nuclei, specifically the impact of synaptic parameters, during both sleep and waking conditions. On the basis of the circuit proposed by Canedo et al. (2000), we have developed realistic computational models that have been tested with simultaneous electrocorticographic as well as intracellular cuneate recordings performed in anaesthetized cats. The results show that, (1) under sleep conditions, the model can block the transmission of afferent sensory information and, (2) operations expected during wakefulness, such as filtering and facilitation, can be performed if synaptic parameters are appropriately tuned.Argentina. Consejo Interinstitucional de Ciencia y Tecnología; PB01-121212Xunta de Galicia; XU02–211

Ensemble and continual federated learning for classifcation tasks

Federated learning is the state-of-the-art paradigm for training a learning model collaboratively across multiple distributed devices while ensuring data privacy. Under this framework, different algorithms have been developed in recent years and have been successfully applied to real use cases. The vast majority of work in federated learning assumes static datasets and relies on the use of deep neural networks. However, in real world problems, it is common to have a continual data stream, which may be non stationary, leading to phenomena such as concept drift. Besides, there are many multi-device applications where other, non-deep strategies are more suitable, due to their simplicity, explainability, or generalizability, among other reasons. In this paper we present Ensemble and Continual Federated Learning, a federated architecture based on ensemble techniques for solving continual classification tasks. We propose the global federated model to be an ensemble, consisting of several independent learners, which are locally trained. Thus, we enable a flexible aggregation of heterogeneous client models, which may differ in size, structure, or even algorithmic family. This ensemble-based approach, together with drift detection and adaptation mechanisms, also allows for continual adaptation in situations where data distribution changes over time. In order to test our proposal and illustrate how it works, we have evaluated it in different tasks related to human activity recognition using smartphonesOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This research has received financial support from AEI/FEDER (European Union) Grant Number PID2020-119367RB-I00, as well as the Consellería de Cultura, Educación e Universitade of Galicia (accreditation ED431G-2019/04, ED431G2019/01, and ED431C2018/29), and the European Regional Development Fund (ERDF). It has also been supported by the Ministerio de Universidades of Spain in the FPU 2017 program (FPU17/04154)S

An extensive experimental survey of regression methods

Regression is a very relevant problem in machine learning, with many different available approaches. The current work presents a comparison of a large collection composed by 77 popular regression models which belong to 19 families: linear and generalized linear models, generalized additive models, least squares, projection methods, LASSO and ridge regression, Bayesian models, Gaussian processes, quantile regression, nearest neighbors, regression trees and rules, random forests, bagging and boosting, neural networks, deep learning and support vector regression. These methods are evaluated using all the regression datasets of the UCI machine learning repository (83 datasets), with some exceptions due to technical reasons. The experimental work identifies several outstanding regression models: the M5 rule-based model with corrections based on nearest neighbors (cubist), the gradient boosted machine (gbm), the boosting ensemble of regression trees (bstTree) and the M5 regression tree. Cubist achieves the best squared correlation (R2) in 15.7% of datasets being very near to it, with difference below 0.2 for 89.1% of datasets, and the median of these differences over the dataset collection is very low (0.0192), compared e.g. to the classical linear regression (0.150). However, cubist is slow and fails in several large datasets, while other similar regression models as M5 never fail and its difference to the best R2 is below 0.2 for 92.8% of datasets. Other well-performing regression models are the committee of neural networks (avNNet), extremely randomized regression trees (extraTrees, which achieves the best R2 in 33.7% of datasets), random forest (rf) and ε-support vector regression (svr), but they are slower and fail in several datasets. The fastest regression model is least angle regression lars, which is 70 and 2,115 times faster than M5 and cubist, respectively. The model which requires least memory is non-negative least squares (nnls), about 2 GB, similarly to cubist, while M5 requires about 8 GB. For 97.6% of datasets there is a regression model among the 10 bests which is very near (difference below 0.1) to the best R2, which increases to 100% allowing differences of 0.2. Therefore, provided that our dataset and model collection are representative enough, the main conclusion of this study is that, for a new regression problem, some model in our top-10 should achieve R2 near to the best attainable for that problemThis work has received financial support from the Erasmus Mundus Euphrates programme [project number 2013-2540/001-001-EMA2], from the Xunta de Galicia (Centro singular de investigación de Galicia, accreditation 2016–2019) and the European Union (European Regional Development Fund — ERDF), Project MTM2016–76969–P (Spanish State Research Agency, AEI)co-funded by the European Regional Development Fund (ERDF) and IAP network from Belgian Science PolicyS

Walking Recognition in Mobile Devices

Presently, smartphones are used more and more for purposes that have nothing to do with phone calls or simple data transfers. One example is the recognition of human activity, which is relevant information for many applications in the domains of medical diagnosis, elderly assistance, indoor localization, and navigation. The information captured by the inertial sensors of the phone (accelerometer, gyroscope, and magnetometer) can be analyzed to determine the activity performed by the person who is carrying the device, in particular in the activity of walking. Nevertheless, the development of a standalone application able to detect the walking activity starting only from the data provided by these inertial sensors is a complex task. This complexity lies in the hardware disparity, noise on data, and mostly the many movements that the smartphone can experience and which have nothing to do with the physical displacement of the owner. In this work, we explore and compare several approaches for identifying the walking activity. We categorize them into two main groups: the first one uses features extracted from the inertial data, whereas the second one analyzes the characteristic shape of the time series made up of the sensors readings. Due to the lack of public datasets of inertial data from smartphones for the recognition of human activity under no constraints, we collected data from 77 different people who were not connected to this research. Using this dataset, which we published online, we performed an extensive experimental validation and comparison of our proposalsThis research has received financial support from AEI/FEDER (European Union) grant number TIN2017-90135-R, as well as the Consellería de Cultura, Educación e Ordenación Universitaria of Galicia (accreditation 2016–2019, ED431G/01 and ED431G/08, reference competitive group ED431C2018/29, and grant ED431F2018/02), and the European Regional Development Fund (ERDF). It has also been supported by the Ministerio de Educación, Cultura y Deporte of Spain in the FPU 2017 program (FPU17/04154), and the Ministerio de Economía, Industria y Competitividad in the Industrial PhD 2014 program (DI-14-06920)S

Concept drift detection and adaptation for federated and continual learning

Smart devices, such as smartphones, wearables, robots, and others, can collect vast amounts of data from their environment. This data is suitable for training machine learning models, which can significantly improve their behavior, and therefore, the user experience. Federated learning is a young and popular framework that allows multiple distributed devices to train deep learning models collaboratively while preserving data privacy. Nevertheless, this approach may not be optimal for scenarios where data distribution is non-identical among the participants or changes over time, causing what is known as concept drift. Little research has yet been done in this field, but this kind of situation is quite frequent in real life and poses new challenges to both continual and federated learning. Therefore, in this work, we present a new method, called Concept-Drift-Aware Federated Averaging (CDA-FedAvg). Our proposal is an extension of the most popular federated algorithm, Federated Averaging (FedAvg), enhancing it for continual adaptation under concept drift. We empirically demonstrate the weaknesses of regular FedAvg and prove that CDA-FedAvg outperforms it in this type of scenarioThis research has received financial support from AEI/FEDER (EU) grant number TIN2017-90135-R, as well as the Consellería de Cultura, Educación e Ordenación Universitaria of Galicia (accreditation 2016–2019, ED431G/01 and ED431G/08, reference competitive group ED431C2018/29, and grant ED431F2018/02), and the European Regional Development Fund (ERDF). It has also been supported by the Ministerio de Universidades of Spain in the FPU 2017 program (FPU17/04154)S