The Linguistics, Discourse and Cognition Research Group, LINDICO, assumes cognitive approaches to combine at all times strictly theoretical linguistic reflection with the necessary promotion of various applied fields, from the conviction that the ultimate goal of the (necessary) grammatical and pragmatic theory is to serve as a basis for subsequent applications and actions of scientific transferability to society.

Although the research caarried out by the members of the group in the different competitive R&D projects covers many fields, the most consolidated studies refer mainly to two lines of work: the field of clinical linguistics and the analysis of political and media discourse. Throughout its trajectory, the group has been consolidating its own theoretical model, with a pragmatic-functionalist orientation, which is framed within the framework of Cognitive Linguistics. Thus, the theoretical areas addressed include all the disciplines of linguistics: phonology, morphosyntax, semantics, pragmatics, typology and universals, psycholinguistics and sociolinguistics. The applied fields include, among others:

• Analysis of discourse in the public sphere, according to different variables and context (politcal discourse, media discourse, digital discourse), with special attention to the argumentation and persuasion that pragmatically characterise the registers of the media and political issuers (parties and leaders) in the different media and communication channels (written press, social media, television, advertising and propaganda, etc.). 
• Clinical linguistics: description of language (grammar and pragmatics) in different pathological situations based on ecological data. This line of research has resulted in initiatives such as 
  • the elaboration of specific corpora of child language and deficient language based on ecological data;
  • the description of the language of pathological situations such as aphasia, Williams syndrome, ADHD, Alzheimer's type dementias, or right hemisphere lesions;
  • the development of various language assessment tests and profiles, and of communication guides for interlocutors of speakers with deficits.

All these lines of research are complemented by the appropriate R&D&I dissemination and management activities, such as conferences, seminars, etc.

The research activity of the group seeks to understand the various structural elements that constitute the Universe, such as black holes, stars, galaxies and the large-scale structure of the Universe, and their mutual interrelation. Astrophysics and Cosmology, traditionally observational disciplines, have undergone major developments in recent decades thanks to the advent of supercomputers. These large scientific infrastructures, like virtual laboratories, make it possible to use sophisticated numerical simulation programmes to develop and test theoretical models by comparing them with observational data obtained with the most modern telescopes. It is precisely in this cutting-edge area of scientific computing that the research work of our group is framed. The fields of study include:

1. Relativistic astrophysical jets produced in different scenarios, such as active galaxy nuclei and massive binary stars.
2. Astrophysical sources of gravitational radiation. The aim is to calculate the emission of gravitational radiation produced in the growth process on neutron stars and black holes, in the gravitational collapse of magnetised and rotating stellar nuclei, in the pulsations of relativistic and rapidly rotating stars and in binary neutron star systems.
3. Cosmology, with special interest in the formation and evolution of galaxies and its mediating role between stellar astrophysics and large-scale cosmology.

The members of the group have extensive experience in the development, optimisation and parallelisation of simulation programs based on different numerical techniques (finite difference/volume methods for the equations of classical and relativistic hydrodynamics and magnetohydrodynamics, N-body techniques, AMR, Numerical Relativity,...). They also have regular access to high-performance computing infrastructures (Spanish Supercomputing Network, PRACE,...). This research activity is carried out in close collaboration with observational and/or experimental groups. 

The group also has a wide network of collaborators in numerous research centres, including the Astrophysics Institutes of the Canary Islands and Andalusia, the Astrophysics Department of the Complutense University, the Department of Astronomy and Meteorology of the University of Barcelona, the Department of Physics of the Aristotle University of Thessaloniki, the Max Planck Institute for Astrophysics (Garching, Germany), Radioastronomy (Bonn, Germany) and Radioastronomy (Bonn, Germany) and Gravitation Physics (Golm, Germany), the Observatories of Paris (Meudon, France) and Trieste (Trieste, Italy) and the Institute for Computational Cosmology (Durham, UK). 

All members of the research group have received continuous funding from regional, national and/or European programmes since the beginning of their research.

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.