The main objective of the QCEXVAL group is to determine, with high precision, chemical mechanisms derived from the interaction between visible-UV radiation and relevant molecular systems in biology, medicine, nanotechnology and the environment, thus establishing the basis for predicting innovative electronic properties and proposing new molecules for their applied use in these fields. To this end, the tools of theoretical and computational chemistry and computationally powerful computer farms are used. Furthermore, we contribute to the development of new methodologies and computational procedures to solve highly complex problems.

Our research group works on prediction and optimisation techniques. Prediction of future observations and optimisation in order to build automatic decision support tools in an uncertain environment. This has already led to interesting results, especially in the fields of medicine and public health, but also in industry and finance. Consequently, our purpose is twofold: to deepen the study of prediction models that can incorporate temporal and/or spatial relationships, as well as covariates, and to continue advancing in the development of methodologies that incorporate uncertainty in optimisation, which will allow us to build new tools to support decision-making in different areas of planning and management. In particular, we will investigate the analysis of scenarios with the presence of uncertainty for which there is information given by a series of historical data, for which we will use time series models, and those in which in addition to the temporal relationship there is a neighbourhood relationship between contemporaneous observations, for which we will use spatio-temporal models. Previous work have considered prediction models based on dynamic state-space models with innovations, and have advanced theoretically in the incorporation of multistationarity, covariates and autocorrelated errors, with the aim that they can be used in the automatic prediction of banks of time series. Recently, we have introduced fuzzy time series methods to obtain as a prediction a fuzzy number that can be incorporated into optimisation models using fuzzy logic to incorporate uncertainty. Our aim is to extend the field of application of these methodologies by means of simulation-optimisation models that make it possible to analyse the quality of the solutions and the optimisation of the objectives. We are also working on compositional time series, which allow us to contextualise mixed models for multivariate longitudinal compositional data in a microbiome setting. 

Recently, we have been working on the combination of forecasts, using weighted averages of forecasts obtained with different methods, with the aim of designing a decision support system that allows the adjustment of weights and the optimal selection of prediction models to obtain more reliable forecasts. The study of the geographical and temporal variability of health phenomena is currently very popular in the world of epidemiology. Numerous risk smoothing models that simultaneously incorporate the spatial dependence of risks between nearby regions and the temporal dependence of risks for each of the regions have been proposed in recent years. Classical models have been designed for retrospective analysis of incidence time series and therefore do not address fundamental issues from the point of view of planning preventive and control actions such as predicting the onset of outbreaks, predicting epidemic peaks and ending epidemics. We want to analyse some problems related to the prediction of the spread of epidemics, focusing simultaneously on the spatial and temporal components of the problem. Along the same lines, simultaneous incorporation of temporal and spatial components in the models studied, we are working on various scenarios: Bayesian hierarchical model extension, recently proposed by members of our team, which allows estimating risks and detecting clusters simultaneously where it is considered that the spatial dependence between relative risks does not necessarily conform to neighbourhood criteria; spatio-temporal extension of Bayesian stochastic compartment models; proposal of a spatio-temporal model for the treatment of epidemiological data in the compositional scenario, and proposal of a spatio-temporal model for the analysis of associations between environmental exposures and health. All these prediction results will be used in the construction of decision support systems based on optimisation models under uncertainty for different domains. Among them, in addition to the areas mentioned above, we will work on financial management problems, in particular the problem of portfolio selection. The portfolio selection problem is about determining an optimal portfolio that satisfies the decision-maker's preferences, in terms of risk and return on investment. We have introduced the approximation of portfolio performance by credibility distributions and alternative measures of investment risk. We will address the portfolio selection problem by introducing a loss function that can be visualised by the investor as a measure of his preferences and develop evolutionary multi-objective optimisation strategies to determine efficient portfolios for different investor risk profiles.

The objective of our group is the methodological and applied research of Bayesian Statistics, especially in scenarios of epidemiological and environmental type. Our work is fundamentally based on three axes:

1. Hierarchical models in studies with correlated data.
2. Model selection.
3. Computational simulation models.

All of them are somewhat mixed in nature, methodological and applied, and in the compatibility and interrelation of many of their knowledge and objectives.

The first thematic block is the most extensive and is dedicated to research on models with correlated data associated with structures of a spatial-temporal, longitudinal, survival or non-survival type, and of blood kinship. Methodological research in disease mapping has a long tradition in our team, currently with unbalanced multivariate and spatial-temporal objectives. This block also contains new research proposals dedicated to joint models with longitudinal and survival data, methodology on species distribution, spatial-temporal surveillance of diseases and regression methods for scattered genetic data from genetically isolated populations with known family trees, which will undoubtedly lead us to Big Data.

In the area dedicated to the selection of models, marginal, conditioned, and combined measures are studied to quantify the contribution of a potential set of covariates in the explanation of a response of interest, and two new lines have been initiated, with a more applied orientation, which link the subject of variable selection with longitudinal and survival models and related data through structures of consanguinity.

Finally, in the block of computational models, the group continues with a line of action dedicated to the calibration of multivariate computational models and the implementation of the results obtained in a computer application and in a new application dedicated to uncertainty modelling in compartmental models.

Corpus linguistics (CL), with its decidedly empirical approach to language research, has greatly enriched previous paradigms to the point of becoming an obligatory methodological reference in the current landscape of linguistic studies. 

We are interested in highlighting two strands, one dealing with developments in corpus linguistics and the other focusing on its applications. Like other empirical research within linguistics, LC research straddles the humanities and the social sciences, on the basis of computational linguistics. From the humanities it takes its primary interest in the study of language in its multiple aspects, from the social sciences it has taken a large part of its methodology based on quantification (mathematics, statistics, etc.), and from computer science, the development of increasingly sophisticated analytical tools. In this respect, the methodologies used in LC, far from being static, continue to evolve and incorporate important developments, whether through the creation of increasingly sophisticated software packages in ad hoc corpus research, the creation of specific portals, or the creation of tools focused on a variety of research tasks.

Research on LC developments is related to qualitative analysis methods, to textual annotation and to the use of quantitative analysis. In addition, some recent computer science developments, such as so-called sentiment analysis or opinion mining, have turned their interest to the analysis of large amounts of data on the web (big data).

In terms of applications, corpus linguistics has no limits, its great strength being the investigation of large databases that the analyst cannot manipulate effectively through manual analysis. LC is now being applied to any area of linguistic research, be it digital genres of any kind or non-digital genres. In the case of non-digital genres, the solution necessarily involves digitisation, since LC necessarily operates on digitised texts. However, although LC is the fundamental methodology for many researchers, it does not dispense with qualitative or manual analysis, and in its scientific production it is articulated in synergies with other approaches. It is very difficult today to conceive of a dictionary or a grammar without corpus research. But beyond lexicography and phraseology, which have grown hand in hand with the corpus, we find applications in all types of linguistic analysis, whether pragmatic or discursive, including, more recently, stylistic analysis. Not forgetting applications to the acquisition and teaching of second languages, or research into specialised languages. Nor should we forget the invaluable contribution of LC to translatology, given that the corpus is a fundamental tool for translators. There are real networks of researchers working on specific aspects. 

However, returning to the starting point, we are interested in focusing our research on those aspects that evaluate the strength of proposals based on techniques developed within corpus linguistics in research on different fronts.

The aim of the project is to study functions with smooth properties defined in Banach spaces as well as the existence of equivalent norms with "better" properties than the initial one (renormings). This study refers both to the influence that the geometric characteristics, or topological properties, of the space have on the differentiability of the norm or the existence of renormings with certain properties, and to the study of various classes of smooth functions, whether analytic, differentiable or polynomial, as Banach spaces or algebras and the operators that can be defined between them.

This line of research studies the intrinsic properties of neutrinos. It investigates the phenomenon of oscillations between neutrino families by measuring the parameters that define these oscillations and tries to elucidate the nature of the neutrino, i.e. whether it is a Dirac fermion or a Majorana fermion. IFIC is leading the NEXT experiment to search for double beta decays without neutrino production, the detection of which would imply that the neutrino is a Majorana particle. It is also involved in several neutrino oscillation experiments with accelerators: the T2K experiment, which is being carried out in Japan, and the next-generation DUNE experiment in the United States. In addition, the IFIC neutrino group develops advanced nuclear instrumentation, which could have important industrial applications, in particular in medical physics and nuclear proliferation control.

The team works in four main related and simultaneously complementary research lines: 
- Pattern Recognition Techniques, Computer Vision and its applications for different problems, mainly content-based image search, distance learning and emotion detection from video sequences.
- Processing of different types of signals, particularly audio and video ones. Group works in this research line include capture, analysis and synthesis of acoustic signals, as well as analysis of video signals, all acting in coordination with the Computer Vision line.
- Design of Intelligent Tutoring Systems (ITSs) with affective capacities. Part of the results obtained from the first and second line of work are used to detect emotions from videos filmed with low-cost hardware by applying techniques specific to the fields of Signal Processing and Computer Vision.
- High-performance and high-availability computer systems offering a fundamental tool for all the previously mentioned fields (pattern recognition, intelligent systems and signal processing) by providing the necessary power in real time whenever required from application areas or in case of high system availability requirements. In this regard, the latest trends focus on distributed systems, the handling of Big Data and the so-called “cloud computing”:  three fundamental aspects in the context of group investigation.

The ISP research group, http://isp.uv.es, has a long tradition in statistical analysis of data coming from imaging systems. These measurements depend on the properties of the scenes and the physics of the imaging process, and their relevance depends on the (natural or artificial) observer that will analyze the data. Our distinct approach to signal, image and vision processing combines machine learning theory with the understanding of the underlying physics and biological vision. Applications mainly focus on optical remote sensing and computational visual neuroscience. Empirical statistical inference, also known as machine learning, is a field of computer science interested in making predictions, and models from observations and sensory data. The information processing tools in machine learning are critical to understand the function of natural neural networks involved in biological vision, as well as to make inferences in complex dynamic network systems, such as the Earth biosphere, atmosphere, and ecosystems. 

Problems in Visual Neuroscience and in Remote Sensing based geosciences require similar mathematical tools. For example, both scientific fields face model inversion and model understanding problems. In both cases, one has a complex forward model that is difficult to invert (to extract information from) either because it is not analytically invertible (undetermined) or because the measurements (or responses) are noisy in nature. In Remote Sensing, the forward model is the imaging process given certain state conditions in the surface and atmosphere. In Visual Neuroscience, the forward model includes what is known in the neural pathway from the retina to the different regions of the visual cortex. Inversion of such models is key to make quantitative and meaningful inferences about the underlying system that generated the observed data. Beyond such quantitative assessment, a qualitative interpretation of the proposed models is mandatory as well. Qualitative understanding is more challenging than prediction, and causal inference from empirical data is the common playground both in geoscience and neuroscience. Simultaneous observations and recordings from a phenomenon lead to multidimensional signals that may display strong statistical correlation between the components. However, correlation is not enough to establish cause-effect relationships. This is key when analyzing activation and inhibition in the communication between different brain regions, and it is also of paramount relevance when studying the causes, effects and confounders of essential climate variables for detection and attribution in climate science. Finally, another parallelism is the analysis of big visual data: hyperspectral imagery acquired by current and upcoming satellite sensors pose a big-data information processing problem in similar ways to that in the visual brain. Adaptation, pattern recognition, inference and decision making in the brain may be quite inspiring for remote sensing image analysis. 

The group is therefore organized into a theoretical research branch (A) and a more applied research branch (B). The theoretical machine learning core tackles model inversion, interpretation, causal inference from empirical data and inclusion of physical constraints and prior knowledge in big visual data. The applied research lines are devoted to apply and adapt the theoretrical developments for remote sensing, geociences and visual neuroscience. For the sake of simplicity, we have grouped together these activities along five conceptual research lines: machine learning, visual neuroscience, image processing, remote sensing and big data processing.

The main purpose of IDAL is the study and application of intelligent methods of data analysis for pattern recognition, with applications that struggle with prediction, classification or trend determination.

Its members apply classic statistical methods and automatic learning techniques to large databases: statistical hypothesis testing, linear models, feature selection and extraction, neural networks, clustering algorithms, decision trees, support vector machines, probabilistic graphical models, manifold visualization, fuzzy logic, reinforcement learning, etc.

The ultimate goal of the application of these methods is to generate mathematical models which enable the optimization of processes and resources, as well as to reach the optimal decision making stage. A clear example of this is the area of health, where IDAL has developed clinical decision support application based on data analysis. These applications make it possible to improve the patient’s quality of life (establishing optimal clinical guidelines) while reducing healthcare costs.

Complementing this knowledge, the group has extensive experience in signal processing (spectral analysis, digital filter, adaptive process, etc.) due to their work of over 10 years in biosignal processing (mainly ECG and EEG). With all this background, IDAL is able to analyse a wide range of data and signals. This fact is backed up by the large number of both private and public contracts it has developed in different areas of knowledge. Furthermore, most of the practical work carried out has been displayed in important scientific publications with high impact parameters and in a large number of communications to international congresses within the area of data analysis.

Among the developed applications, (outside the health area already mentioned) are the following, i.a: web recommendations, models for optimal incentive management to gain customer loyalty, measurement-based shoe recommendations, and other data analysis consultancy works. In addition to its practical work IDAL, it develops new data analysis algorithms improving the performance of the existing ones. This research work is also reflected in a wide dissemination in the form of different publications in journals of impact and in congresses of data analysis relevant to the scientific community.

The Networks and Virtual Environments (GREV) group focuses its research on distributed systems interconnection networks, distributed virtual environments and the development of scalable multi-agent systems. These three types of systems are transversal to different types of applications, but we have mainly applied them to areas as diverse as archaeology, urban mobility, online videogames, computational genetics and social sciences.

Since its foundation, the GREV has been conducting its research integrated and in permanent collaboration with the Consolideer ACCA team, composed by research groups from four Spanish universities (the GREV and groups from the UPV, UCLM and UMU). Over the past few years, the group has initiated interdisciplinary collaborations with research groups in the fields of Computational Genomics and Social Sciences.

The research objectives of our group are the design and analysis of numerical methods that can make efficient use of available computational resources by adapting them to data from a numerical simulation of predominantly convective processes or from adaptive image and signal processing using multiresolution techniques.

The numerical methods for partial differential equations that we design and analyse are used in fields as diverse as gas dynamics, both compressible and incompressible, sedimentation of polydisperse suspensions, flows in porous media, dynamics of charged particles, shallow water models, etc. The ability to use approximation techniques within a multi-resolution environment has proven extremely useful in the compression of strongly gradient signals in applications such as image restoration, disparity calculations or optical flow. Techniques specifically designed to preserve other data properties, such as convexity and monotonicity, are also used, which is crucial in the data generation process in various applications.

The research group LSyM focuses its activity on simulation systems development, employing the latest Virtual Reality techniques. LSyM has always worked looking for close collaboration with the company and obtaining important results in the field of civil works. The group is a part of the Institute on Robotics and Information and Communications Technologies (IRTIC) of the Universitat de València. 
Lines of research: 

• Integration of the real-time immersive simulators: design of all simulator’s elements, including both hardware and software (dynamic models of objects and 3-D scenarios). 
• Development of e-learning platforms based on 3-D simulation: simulation technologies based on WebGL and Unity-3D in order to implement virtual 3-D environments, executable from the browser on different computing platforms. Use of Moodle and other e-learning standards 
• Advanced computing in graphics processing units (GPUs): Development of performance league calculation programmes based on CUDA, OpenCL and shaders, that run on GPU network architectures. 
• Real-time physical modelling: Development of simulation and of models of collaborative behaviour between avatars. 

Fields of application: 

• Industrial: Virtual and augmented reality systems in several industrial areas (transport, railway sector, construction, maritime sector, etc.).
• Education: Web-based simulation of learning environments, e-learning platforms for training and evaluation courses.

Services to companies and other entities: 

Technical assistance and consulting on: 

• Development of real-time virtual environments to train operators of industrial machinery, cranes, civil engineering machinery and vehicles.
• Counselling on the integration of low-medium-high cost simulators and on the choice of the appropriate hardware for the app. 
• Design and implementation of training systems based on the use of simulators in different areas (transport, heavy machinery, air traffic controllers, etc.) and focused on learning risk prevention techniques.
• Development of e-learning platforms

The TAPEC group researches on hardware devices and architectures for computer perception, with a special focus on image processing and vision devices. In the field of perception architectures, research is carried out on the use of FPGAs for image and visual event processing. In recent years, architectures have been developed that work with pixel or event streams instead of typical image processing.

The most active field of research at present is the design and fabrication of event-driven vision cameras. The TAPEC group has developed a proprietary chip, in CMOS technology, that sends the pixel that has changed the most since it was last read, instead of sending the pixels in order as in conventional images. This technique has advantages such as selective reduction of the information to be processed, simple and synchronous interface, high processing speed and motion analysis, etc. As an example, the latest camera developed is capable of tracking objects with a temporal resolution of microseconds; to achieve the same with a conventional camera, it would have to operate at a rate of 500,000 images per second, with hardware capable of processing all that information in real time, which is not feasible today.

The group has extensive research experience in the field of Theoretical and Computational Chemistry, as well as in the areas of grid computing and eScience. The research activity of the group has mainly focused on the application of Theoretical and Computational Chemistry techniques to systems of technological interest. The research focuses on the study of the electronic and optical properties of molecular and polymeric materials for use in molecular electronics and energy storage and includes the development of new methodologies for the study of chemical processes. On the more technical side, the group's efforts have also focused on the implementation of Grid technology, for the efficient and productive use and exploitation of computational resources through scientific grid computing and its application to eScience.

Cotino (Director) has been the PI for three consecutive national projects on these issues, one focused on e-government (2005-2008), another on freedom and participation in the social web (2010-2012) and the current one on Open Government (2013-2015), a subject on which continuity is sought. The new R&D&I project of the Ministry of Economy and Competitiveness is pending resolution. It is entitled “The progress of Open Government. Constitutional legal regime of the implementation of transparency policies, access to information, open data, collaboration and participation, especially through ICTs and e-government” DER2015-65810-P (pending resolution). Cotino is also PI. The results are more than evident, both on the web and in hundreds of publications and papers. Besides some readjustments, this time the internationalisation is boosted by strengthening the foreign team. All the members of the team have worked and cooperated actively in recent years.

This UV group also includes two members who are not part of the Ministry’s project, two point researchers in Spain, Fernández Salmerón and Valero Torrijos, from Administrative Law.

The legal treatment of contents and freedoms on the Internet, liability of providers, hate speech, limits, censorship, etc. will continue to be addressed. On the other hand, given the relatively ethereal concept of open government and the predominance of other disciplines, a constitutional legal treatment is needed to legally decant, sediment and distil all these changes and advances that have just taken place in the last few years with respect to the contents of the concept of Open Government (transparency, participation, collaboration, open data, etc.) and its projection in the legal framework as well as the fundamental rights that are involved. Continuity is sought as we are at a crucial moment in the development of the Open Government. We are on the culmination of a process of fundamentalising the right to transparency and access to public information. This process has not been completed neither by the ECHR, nor the CJEU, nor, especially, by our Constitutional Court and Supreme Court. It is going to be especially important to analyse the interpretation by data protection authorities, transparency authorities, ombudsmen with jurisdiction over this area, and, of course, the ordinary and constitutional courts.

The aim is to analyse and monitor both the application of the transparency law and new administrative laws and reform of the law on re-use, the decree implementing the law on transparency, 2015, as well as the wave of regional laws in the 2013-2015 period in the autonomous community.

Beyond the mere normative analysis, it is a matter of taking into account the practical implementation and policies of open government policies, especially in Spain at the different town, region and state levels. It is a booming issue in 2015-16 in Spain. However, given the transnational nature of the phenomenon, it will also be monitored from a comparative and supranational European and Latin American perspective.

The aim is not only to analyse the constitutional legal development of open government, but also, as far as possible, to influence it on the different legal operators.
It has a solid experience in the dissemination of high quality legal content in classic formats (more than nine collective works and ten congresses), partly through www.derechotics.com or the Cyberlaw clinic and highly visible Twitter profiles of some of the members. In addition to generating and disseminating academic knowledge, it will be conducted an observatory of e-administration 2.0, an analysis of websites of Spanish and international reference; a document of practical legal recommendations and precautions.