Implementació de cursos d'Electrònica i Tècniques de Microscòpia en modalitat docent semipresencial amb estratègies de treball cooperatiu i avaluació continuada en entorn de programari lliure Moodle

L’objectiu principal del present projecte MQD és la implementació de cursos semipresencials en el campus virtual MOODLE per a la docència de les assignatures d’Electrònica troncals de l’ensenyament de Física que depenen del nostre departament i de l’assignatura Tècniques de Microscòpia, que és optativa i comuna als Màsters de Nanociència i Nanotecnologia i d’Enginyeria Física. Plantegem metodologies docents basades en: (1) reduir la presencialitat, (2) afavorir l'autoaprenentatge, (3) aplicar estratègies d’avaluació formativa i avaluació acreditativa continuada i (4) fer ús de les TIC com a suport a la docència. La versatilitat de la plataforma Moodle per compartir recursos permetrà generar un material docent accessible per altres professors. D’altra banda, la possibilitat de Moodle per a la gestió de grups, organització i revisió de tasques facilitarà el seguiment de l’activitat d’autoaprenentatge i de treball cooperatiu així com de l’avaluació final dels aprenentatges. Durant la duració d'aquest projecte MQD s'han implementat els segúents entorns: - Curs d'Electrònica Física, semipresencial, amb treball cooperatiu i amb implantació d'avaluació continuada - Entorn de Coordinació del Master de Nanociència i Nanotecnologia - Curs de l'assignatura optativa de Màster Oficial de Nanociència i Nanotecnologia "Tècniques de Microscòpia"

Local-global splitting for spatiotemporal-adaptive multiscale methods

We present a novel spatiotemporal-adaptive Multiscale Finite Volume (MsFV) method, which is based on the natural idea that the global coarse-scale problem has longer characteristic time than the local fine-scale problems. As a consequence, the global problem can be solved with larger time steps than the local problems. In contrast to the pressure-transport splitting usually employed in the standard MsFV approach, we propose to start directly with a local-global splitting that allows to locally retain the original degree of coupling. This is crucial for highly non-linear systems or in the presence of physical instabilities. To obtain an accurate and efficient algorithm, we devise new adaptive criteria for global update that are based on changes of coarse-scale quantities rather than on fine-scale quantities, as it is routinely done before in the adaptive MsFV method. By means of a complexity analysis we show that the adaptive approach gives a noticeable speed-up with respect to the standard MsFV algorithm. In particular, it is efficient in case of large upscaling factors, which is important for multiphysics problems. Based on the observation that local time stepping acts as a smoother, we devise a self-correcting algorithm which incorporates the information from previous times to improve the quality of the multiscale approximation. We present results of multiphase flow simulations both for Darcy-scale and multiphysics (hybrid) problems, in which a local pore-scale description is combined with a global Darcy-like description. The novel spatiotemporal-adaptive multiscale method based on the local-global splitting is not limited to porous media flow problems, but it can be extended to any system described by a set of conservation equations.

Aplicativo web para la corrección de modelos UML/OCL

Introducció d'un entorn virtual d' autoaprenentatge que permeti als estudiants millorar les seves habilitats de modelització, una peça clau en la seva capacitació com a professionals de la informàtica adaptats a les demandes de la societat actual.

Object and Scene Classification: what does a Supervised Approach Provide us?

Given a set of images of scenes containing different object categories (e.g. grass, roads) our objective is to discover these objects in each image, and to use this object occurrences to perform a scene classification (e.g. beach scene, mountain scene). We achieve this by using a supervised learning algorithm able to learn with few images to facilitate the user task. We use a probabilistic model to recognise the objects and further we classify the scene based on their object occurrences. Experimental results are shown and evaluated to prove the validity of our proposal. Object recognition performance is compared to the approaches of He et al. (2004) and Marti et al. (2001) using their own datasets. Furthermore an unsupervised method is implemented in order to evaluate the advantages and disadvantages of our supervised classification approach versus an unsupervised one

Desenvolupament en Moodle d’eines d’autoaprenentatge i avaluació continua en les àrees de Física i Matemàtiques

Aquesta comunicació presenta el projecte Aula de Tests desenvolupat com a suport en el desplegament de les assignatures de física i matemàtiques de primer curs dels estudis d'enginyeria de l’Escola Superior Politècnica de la Universitat Pompeu Fabra. El projecte té com a objectiu dissenyar eines d'auto aprenentatge i d'avaluació contínua accessible on-line a través de l'entorn Moodle per a afavorir el procés d'aprenentatge de l’estudiant. El context d’aquesta experiència es caracteritzaper la inherent dificultat dels estudis d’enginyeria, pel fet que molts estudiants entren a la universitat amb mancances substancials de coneixements en aquestes àrees així com la heterogeneïtat en quant a la formació pre-universitària. S’hi descriuen lescaracterístiques de les activitats programades i el context on s’han aplicat i es presenten els resultats de satisfacció, participació i notes que aporten informació útil al professorat per a adequar la planificació i les activitats els cursos següents.

Decision making in uncertain and changing environments

We consider an agent who has to repeatedly make choices in an uncertainand changing environment, who has full information of the past, who discountsfuture payoffs, but who has no prior. We provide a learning algorithm thatperforms almost as well as the best of a given finite number of experts orbenchmark strategies and does so at any point in time, provided the agentis sufficiently patient. The key is to find the appropriate degree of forgettingdistant past. Standard learning algorithms that treat recent and distant pastequally do not have the sequential epsilon optimality property.

Geomodelling of a fluvial system with semi-supervised support vector regression

Fluvial deposits are a challenge for modelling flow in sub-surface reservoirs. Connectivity and continuity of permeable bodies have a major impact on fluid flow in porous media. Contemporary object-based and multipoint statistics methods face a problem of robust representation of connected structures. An alternative approach to model petrophysical properties is based on machine learning algorithm ? Support Vector Regression (SVR). Semi-supervised SVR is able to establish spatial connectivity taking into account the prior knowledge on natural similarities. SVR as a learning algorithm is robust to noise and captures dependencies from all available data. Semi-supervised SVR applied to a synthetic fluvial reservoir demonstrated robust results, which are well matched to the flow performance

Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar¹

Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI) derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13). Excluding the variable profile curvature (set 12), overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.

Application of Multi-SNP Approaches Bayesian LASSO and AUC-RF to Detect Main Effects of Inflammatory-Gene Variants Associated with Bladder Cancer Risk

The relationship between inflammation and cancer is well established in several tumor types, including bladder cancer. We performed an association study between 886 inflammatory-gene variants and bladder cancer risk in 1,047 cases and 988 controls from the Spanish Bladder Cancer (SBC)/EPICURO Study. A preliminary exploration with the widely used univariate logistic regression approach did not identify any significant SNP after correcting for multiple testing. We further applied two more comprehensive methods to capture the complexity of bladder cancer genetic susceptibility: Bayesian Threshold LASSO (BTL), a regularized regression method, and AUC-Random Forest, a machine-learning algorithm. Both approaches explore the joint effect of markers. BTL analysis identified a signature of 37 SNPs in 34 genes showing an association with bladder cancer. AUC-RF detected an optimal predictive subset of 56 SNPs. 13 SNPs were identified by both methods in the total population. Using resources from the Texas Bladder Cancer study we were able to replicate 30% of the SNPs assessed. The associations between inflammatory SNPs and bladder cancer were reexamined among non-smokers to eliminate the effect of tobacco, one of the strongest and most prevalent environmental risk factor for this tumor. A 9 SNP-signature was detected by BTL. Here we report, for the first time, a set of SNP in inflammatory genes jointly associated with bladder cancer risk. These results highlight the importance of the complex structure of genetic susceptibility associated with cancer risk.

Internet-lähteistä haettujen uutisten ryhmittely

Tämän diplomityön tarkoituksena on tutkia, mitä vaaditaan uutisten samanlaisuuden automaattiseen tunnistamiseen. Uutiset ovat tekstipohjaisia uutisia, jotka on haettu eri uutislähteistä. Uutisista on tarkoitus tunnistaa ensinnäkin ne uutiset, jotka tarkoittavat samaa asiaa, sekä ne uutiset, jotka eivät ole aivan sama asia, mutta liittyvät kuitenkin toisiinsa. Tässä diplomityössä tutkitaan, millä algoritmeilla tämä tunnistus onnistuu tehokkaimmin sekä suomalaisessa, että englanninkielisessä tekstissä. Diplomityössä vertaillaan valmiita algoritmeja. Tavoitteena on valita sellainen algoritmiyhdistelmä, että 90 % vertailluista uutisista tunnistuu oikein. Tutkimuksessa käytetään 2 eri ryhmittelyalgoritmia, sekä 3 eri stemmaus-algoritmia. Näitä algoritmeja vertaillaan sekä uutisten tunnistustehokkuuden, että niiden suorituskyvyn suhteen. Parhaimmaksi stemmaus-algoritmiksi osoittautui sekä suomen-, että englanninkielisten uutisten vertailussa Porterin algoritmi. Ryhmittely-algoritmeista tehokkaammaksi osoittautui yksinkertaisempi erilaisiin tunnuslukuihin perustuva algoritmi.

Persistence of Transmitted HIV-1 Drug Resistance Mutations Associated with Fitness Costs and Viral Genetic Backgrounds.

Transmission of drug-resistant pathogens presents an almost-universal challenge for fighting infectious diseases. Transmitted drug resistance mutations (TDRM) can persist in the absence of drugs for considerable time. It is generally believed that differential TDRM-persistence is caused, at least partially, by variations in TDRM-fitness-costs. However, in vivo epidemiological evidence for the impact of fitness costs on TDRM-persistence is rare. Here, we studied the persistence of TDRM in HIV-1 using longitudinally-sampled nucleotide sequences from the Swiss-HIV-Cohort-Study (SHCS). All treatment-naïve individuals with TDRM at baseline were included. Persistence of TDRM was quantified via reversion rates (RR) determined with interval-censored survival models. Fitness costs of TDRM were estimated in the genetic background in which they occurred using a previously published and validated machine-learning algorithm (based on in vitro replicative capacities) and were included in the survival models as explanatory variables. In 857 sequential samples from 168 treatment-naïve patients, 17 TDRM were analyzed. RR varied substantially and ranged from 174.0/100-person-years;CI=[51.4, 588.8] (for 184V) to 2.7/100-person-years;[0.7, 10.9] (for 215D). RR increased significantly with fitness cost (increase by 1.6[1.3,2.0] per standard deviation of fitness costs). When subdividing fitness costs into the average fitness cost of a given mutation and the deviation from the average fitness cost of a mutation in a given genetic background, we found that both components were significantly associated with reversion-rates. Our results show that the substantial variations of TDRM persistence in the absence of drugs are associated with fitness-cost differences both among mutations and among different genetic backgrounds for the same mutation.

A Hierarchical Approach for Multi-task Logistic Regression

Peer-reviewed

Farmacología y toxicología en I+D+i: adquisición de competencias a través de un ejemplo de desarrollo de un fármaco

The implementation of the subject Pharmacology and Toxicology in R+D+i in the Pharmacy Degree, has led to the launch of a new methodological approach and teaching performance with the aim of developing the generic skills of the University of Barcelona (e.g., self-learning, team-working). An additional objective was students' integration of knowledge from different subjects in the degree which form the basis of the preclinical and clinical development of a drug. For this purpose, the teaching strategy used in the development of the subject was based on: 1) re-developing the content that students had been taught previously or were being taught in the same semester as a part of other subjects, and framing them in the environment of the pharmaceutical industry, 2) introducing new and previously unseen contents to do with drug development and toxicology, 3) developing a battery of activities to be undertaken by teams of students relating to the R+D+i of a particular drug. During the development of these activities, students have to acquire generic skills in addition to the subject-specific skills. The results obtained from the student survey give us grounds for satisfaction and allow us to consider that we have reached the goal of improving students' learning in Pharmacology and Toxicology applied to drug development in the pharmaceutical world today.

Itseoppiva liikennevalojen ohjausjärjestelmä

Tutkielma tarjoaa teoreettisen mallin, jossa liikennevalojen ohjausjärjestelmä pyrkii oppimaan liikenteen kaavamaisuuksia itsenäisesti.

Feature selection using Fuzzy Entropy measures with Yu's Similarity measure

In this study, feature selection in classification based problems is highlighted. The role of feature selection methods is to select important features by discarding redundant and irrelevant features in the data set, we investigated this case by using fuzzy entropy measures. We developed fuzzy entropy based feature selection method using Yu's similarity and test this using similarity classifier. As the similarity classifier we used Yu's similarity, we tested our similarity on the real world data set which is dermatological data set. By performing feature selection based on fuzzy entropy measures before classification on our data set the empirical results were very promising, the highest classification accuracy of 98.83% was achieved when testing our similarity measure to the data set. The achieved results were then compared with some other results previously obtained using different similarity classifiers, the obtained results show better accuracy than the one achieved before. The used methods helped to reduce the dimensionality of the used data set, to speed up the computation time of a learning algorithm and therefore have simplified the classification task