Support matrix machine: A review

Support vector machine (SVM) is one of the most studied paradigms in the realm of machine learning for classification and regression problems. It relies on vectorized input data. However, a significant portion of the real-world data exists in matrix format, which is given as input to SVM by reshaping the matrices into vectors. The process of reshaping disrupts the spatial correlations inherent in the matrix data. Also, converting matrices into vectors results in input data with a high dimensionality, which introduces significant computational complexity. To overcome these issues in classifying matrix input data, support matrix machine (SMM) is proposed. It represents one of the emerging methodologies tailored for handling matrix input data. The SMM method preserves the structural information of the matrix data by using the spectral elastic net property which is a combination of the nuclear norm and Frobenius norm. This article provides the first in-depth analysis of the development of the SMM model, which can be used as a thorough summary by both novices and experts. We discuss numerous SMM variants, such as robust, sparse, class imbalance, and multi-class classification models. We also analyze the applications of the SMM model and conclude the article by outlining potential future research avenues and possibilities that may motivate academics to advance the SMM algorithm

Design, synthesis and characterization of multifuncional MOFs. From gas adsorption and photocatalysis to ciss effect and spin-currents.

293 p.El presente resumen hace referencia al trabajo relacionado con el diseño, síntesis y caracterización de redes metal-orgánicas (MOFs) multifuncionales para su posterior estudio de adsorción de gases, fotocatálisis y generación de corrientes de espín. Para ello, se ha trabajado con los siguientes materiales:1) MOFs basados en el ligando 4,6-pirimidina-dicarboxilato (pmdc) para adsorción selectiva de gases. En este caso se ha trabajado con dos MOFs (LiSc-pmdc y NaSc-pmdc) con un sistema poroso tridimensional en los que se ha comprobado la capacidad de adsorción de H2, CO2 y CH4, confirmando que el MOF LiSc-pmdc es el que mejor capacidad de adsorción de CO2 presenta entre sus compuestos análogos.2) MOFs basados en el ligando 5-cianoisoftalato (CNip) para reducción fotocatalítica de CO2. Se ha sintetizado el compuesto CaZn-CNip para posteriormente modificarlo y obtener dos derivados que han permitido el estudio de la estabilidad química del MOF de partida. También se ha podido estudiar su actividad fotocatalítica, obteniendo como resultado un MOF catalizador de un solo uso capaz de reducir CO2 a CO y H2O a H2 bajo luz visible.3) MOFs quirales basados en los ligandos L- o D-tartrato (L/D-tart) para su uso como filtros de espín. Sehan sintetizado cinco pares de MOFs quirales enantiopuros basados en metales lantánidos para el estudio del efecto CISS. Gracias a este efecto presente en materiales quirales, estos MOFs son capaces de distinguir el espín de los electrones, actuando como filtros que permiten el paso de los electrones con un determinado espín mientras que bloquean al otro. Estos resultados se han obtenido mediante microscopía de fuerza atómica conductora polarizada magnéticamente

Design, synthesis and characterization of multifuncional MOFs. From gas adsorption and photocatalysis to ciss effect and spin-currents.

293 p.El presente resumen hace referencia al trabajo relacionado con el diseño, síntesis y caracterización de redes metal-orgánicas (MOFs) multifuncionales para su posterior estudio de adsorción de gases, fotocatálisis y generación de corrientes de espín. Para ello, se ha trabajado con los siguientes materiales:1) MOFs basados en el ligando 4,6-pirimidina-dicarboxilato (pmdc) para adsorción selectiva de gases. En este caso se ha trabajado con dos MOFs (LiSc-pmdc y NaSc-pmdc) con un sistema poroso tridimensional en los que se ha comprobado la capacidad de adsorción de H2, CO2 y CH4, confirmando que el MOF LiSc-pmdc es el que mejor capacidad de adsorción de CO2 presenta entre sus compuestos análogos.2) MOFs basados en el ligando 5-cianoisoftalato (CNip) para reducción fotocatalítica de CO2. Se ha sintetizado el compuesto CaZn-CNip para posteriormente modificarlo y obtener dos derivados que han permitido el estudio de la estabilidad química del MOF de partida. También se ha podido estudiar su actividad fotocatalítica, obteniendo como resultado un MOF catalizador de un solo uso capaz de reducir CO2 a CO y H2O a H2 bajo luz visible.3) MOFs quirales basados en los ligandos L- o D-tartrato (L/D-tart) para su uso como filtros de espín. Sehan sintetizado cinco pares de MOFs quirales enantiopuros basados en metales lantánidos para el estudio del efecto CISS. Gracias a este efecto presente en materiales quirales, estos MOFs son capaces de distinguir el espín de los electrones, actuando como filtros que permiten el paso de los electrones con un determinado espín mientras que bloquean al otro. Estos resultados se han obtenido mediante microscopía de fuerza atómica conductora polarizada magnéticamente

Intrinsic reward driven exploration for deep reinforcement learning

Deep reinforcement learning has become one of the hottest research topics in machine learning. In reinforcement learning, agents interact with the environment and try to maximise the expected cumulative reward. The goal of reinforcement learning is to find a policy to maximise the agent’s total cumulative rewards. Unfortunately, some environments can only provide extremely sparse rewards, so the agent needs to learn a strategy to explore in its environment more efficiently to find these rewards. However, it is known that exploration in complex environments is a key challenge of deep reinforcement learning, especially for tasks where rewards are very sparse. In this thesis, intrinsic reward driven exploration strategies are investigated. The agent driven by this intrinsic reward can explore expeditiously, so as to find the sparse extrinsic rewards provided by the environment. Recently, surprise has been used as an intrinsic reward that encourages systematic and efficient exploration. We first define a novel intrinsic reward function called assorted surprise, and propose Variational Assorted Surprise Exploration (VASE) algorithm to approximate this assorted surprise in a tractable way, with the help of Bayesian neural networks. Then we apply VASE algorithm to continuous control problems and large scale Atari video games respectively. Experimental results show that VASE performs well across these tasks. Then we discover that all surprise based exploration methods will lose exploration efficiency in areas where the environmental transition is discontinuous. To solve this problem, we propose Mutual Information Minimising Exploration (MIME) algorithm. We show that MIME can explore as efficiently as surprise based methods in other areas of the environment but much better in areas with discontinuous transitions

Modelling of expert nurses' pressure sore risk assessment skills as an expert system for in-service training

In the nursing literature to date there have been no reported applications of `cognitive simulation' nor of intelligent Computer Assisted Learning. In Chapter 1 of this thesis a critical review of existing nurse education by computer is used to establish a framework within which to explore the possibility of simulation of thinking processes of nurses on computer. One conclusion from this review which is offered concerns the importance of firstly undertaking reliable study of nursing cognition. The crucial issue is that an understanding must be gained of how expert nurses mentally represent their patients in order that a valid model might be constructed on computer. The construction of a valid computer based cognitive model proves to be an undertaking which occupies the remainder of this thesis. The approach has been to gradually raise the specificity of analysis of the knowledge base of expert and proficient nurses while seeking concurrently to evaluate validity of the findings. Reported in Chapter 2, therefore, are the several experimental stages of a knowledge acquisition project which begins the process of constructing this knowledge base. Discussed firstly is the choice of the skill domain to be studied - pressure sore risk assessment. Subsequently, the method of eliciting from nurses top-level and micro-level descriptors of patients is set out. This account of knowledge acquisition ends with scrutiny of the performance of nurse subjects who performed a comprehensive simulated patient assessment task in order that two groups might be established - one Expert and one Proficient with respect to the nursing task. In Chapter 3, an extensive analysis of the data provided by the simulated assessment experiment is undertaken. This analysis, as the most central phase of the project, proceeds by degrees. Hence, the aim is to `explain' progressively more of the measured cognitive behaviour of the Expert nurses while incorporating the most powerful explanations into a developing cognitive model. More specifically, explanations are sought of the role of `higher' cognition, of whether attribute importance is a feature of cognition, of the point at which a decision can be made, and of the process of deciding between competing patient judgements. Interesting findings included several reliable differences which were found to exist between the cognition of subjects deemed to be proficient and those taken as expert. In the final part of this thesis, Chapter 4, a more formal evaluation of the computer based cognitive model which was constructed and predictions made by it was undertaken. The first phase involved analysis in terms of process and product of decision making of the cognitive model in comparison to two alternative models; one derived from Discriminant Function Analysis and the other from Automated Rule Induction. The cognitive model was found to most closely approximate to the process of decision making of the human subjects and also to perform most accurately with a test set of unseen patients. The second phase reports some experimental support for the prediction made by the model that nurses represent their patients around action-related `care concepts' rather than in terms of diagnostic categories based on superficial features. The thesis concludes by offering some general conclusions and recommendations for further research