Personalized content retrieval in context using ontological knowledge

Personalized content retrieval aims at improving the retrieval process by taking into account the particular interests of individual users. However, not all user preferences are relevant in all situations. It is well known that human preferences are complex, multiple, heterogeneous, changing, even contradictory, and should be understood in context with the user goals and tasks at hand. In this paper, we propose a method to build a dynamic representation of the semantic context of ongoing retrieval tasks, which is used to activate different subsets of user interests at runtime, in a way that out-of-context preferences are discarded. Our approach is based on an ontology-driven representation of the domain of discourse, providing enriched descriptions of the semantics involved in retrieval actions and preferences, and enabling the definition of effective means to relate preferences and context

Nanoparticles for the tratment of osteoporosis

Osteoporosis is by far the most frequent metabolic disease affecting bone. Current clinical therapeutic treatments are not able to offer long-term solutions. Most of the clinically used antiosteoporotic drugs are administered systemically, which might lead to side effects in non-skeletal tissues. Therefore, to solve these disadvantages, researchers have turned to nanotechnologies and nanomaterials to create innovative and alternative treatments. One of the innovative approaches to enhance osteoporosis therapy and prevent potential adverse effects is the development of bonetargeting drug delivery technologies. It minimizes the systemic toxicity and also improves the pharmacokinetic profile and therapeutic efficacy of chemical drugs. This paper reviews the current available bone targeting drug delivery systems, focusing on nanoparticles, proposed for osteoporosis treatment. Bone targeting delivery systems is still in its infancy, thus, challenges are ahead of us, including the stability and the toxicity issues. Newly developed biomaterials and technologies with potential for safer and more effective drug delivery, require multidisciplinary collaboration between scientists from many different areas, such as chemistry, biology, engineering, medicine, etc, in order to facilitate their clinical applications

Personalized information retrieval based on context and ontological knowledge

The article has been accepted for publication and appeared in a revised form, subsequent to peer review and/or editorial input by Cambridge University PressExtended papers from C&O-2006, the second International Workshop on Contexts and Ontologies, Theory, Practice and Applications1 collocated with the seventeenth European Conference on Artificial Intelligence (ECAI)Context modeling has been long acknowledged as a key aspect in a wide variety of problem domains. In this paper we focus on the combination of contextualization and personalization methods to improve the performance of personalized information retrieval. The key aspects in our proposed approach are a) the explicit distinction between historic user context and live user context, b) the use of ontology-driven representations of the domain of discourse, as a common, enriched representational ground for content meaning, user interests, and contextual conditions, enabling the definition of effective means to relate the three of them, and c) the introduction of fuzzy representations as an instrument to properly handle the uncertainty and imprecision involved in the automatic interpretation of meanings, user attention, and user wishes. Based on a formal grounding at the representational level, we propose methods for the automatic extraction of persistent semantic user preferences, and live, ad-hoc user interests, which are combined in order to improve the accuracy and reliability of personalization for retrieval.This research was partially supported by the European Commission under contracts FP6-001765 aceMedia and FP6-027685 MESH. The expressed content is the view of the authors but not necessarily the view of the aceMedia or MESH projects as a whole

Ground state wavefunction of the quantum Frenkel-Kontorova model

The wavefunction of an incommensurate ground state for a one-dimensional discrete sine-Gordon model -- the Frenkel-Kontorova (FK) model -- at zero temperature is calculated by the quantum Monte Carlo method. It is found that the ground state wavefunction crosses over from an extended state to a localized state when the coupling constant exceeds a certain critical value. So, although the quantum fluctuation has smeared out the breaking of analyticity transition as observed in the classical case, the remnant of this transition is still discernible in the quantum regime.Comment: 5 Europhys pages, 3 EPS figures, accepted for publication in Europhys. Letter

Predicting re-finding activity and difficulty

In this study, we address the problem of identifying if users are attempting to re-find information and estimating the level of difficulty of the re- finding task. We propose to consider the task information (e.g. multiple queries and click information) rather than only queries. Our resultant prediction models are shown to be significantly more accurate (by 2%) than the current state of the art. While past research assumes that previous search history of the user is available to the prediction model, we examine if re-finding detection is possible without access to this information. Our evaluation indicates that such detection is possible, but more challenging. We further describe the first predictive model in detecting re-finding difficulty, showing it to be significantly better than existing approaches for detecting general search difficulty

Hybrid Collagenase Nanocapsules for Enhanced Nanocarrier Penetration in Tumoral Tissues

Poor penetration of drug delivery nanocarriers within dense extracellular matrices constitutes one of the main liabilities of current nanomedicines. The conjugation of proteolytic enzymes on the nanoparticle surface constitutes an attractive alternative. However, the scarce resistance of these enzymes against the action of proteases or other aggressive agents present in the bloodstream strongly limits their application. Herein, a novel nanodevice able to transport proteolytic enzymes coated with an engineered pH-responsive polymeric is presented. This degradable coat protects the housed enzymes against proteolytic attack at the same time that it triggers their release under mild acidic conditions, usually present in many tumoral tissues. These enzyme nanocapsules have been attached on the surface of mesoporous silica nanoparticles, as nanocarrier model, showing a significatively higher penetration of the nanopartides within 3D collagen matrices which housed human osteosarcoma cells (HOS). This strategy can improve the therapeutic efficacy of the current nanomedicines, allowing a more homogeneous and deeper distribution of the therapeutic nanosystems in cancerous tissues

Silica Materials for Medical Applications

The two main applications of silica-based materials in medicine and biotechnology, i.e. for bone-repairing devices and for drug delivery systems, are presented and discussed. The influence of the structure and chemical composition in the final characteristics and properties of every silica-based material is also shown as a function of the both applications presented. The adequate combination of the synthesis techniques, template systems and additives leads to the development of materials that merge the bioactive behavior with the drug carrier ability. These systems could be excellent candidates as materials for the development of devices for tissue engineering

On Nonlinear Stochastic Balance Laws

We are concerned with multidimensional stochastic balance laws. We identify a class of nonlinear balance laws for which uniform spatial $BV$ bounds for vanishing viscosity approximations can be achieved. Moreover, we establish temporal equicontinuity in $L^1$ of the approximations, uniformly in the viscosity coefficient. Using these estimates, we supply a multidimensional existence theory of stochastic entropy solutions. In addition, we establish an error estimate for the stochastic viscosity method, as well as an explicit estimate for the continuous dependence of stochastic entropy solutions on the flux and random source functions. Various further generalizations of the results are discussed

Training a perceptron in a discrete weight space

On-line and batch learning of a perceptron in a discrete weight space, where each weight can take $2 L+1$ different values, are examined analytically and numerically. The learning algorithm is based on the training of the continuous perceptron and prediction following the clipped weights. The learning is described by a new set of order parameters, composed of the overlaps between the teacher and the continuous/clipped students. Different scenarios are examined among them on-line learning with discrete/continuous transfer functions and off-line Hebb learning. The generalization error of the clipped weights decays asymptotically as $exp(-K \alpha^2)$/$exp(-e^{|\lambda| \alpha})$ in the case of on-line learning with binary/continuous activation functions, respectively, where $\alpha$ is the number of examples divided by N, the size of the input vector and $K$ is a positive constant that decays linearly with 1/L. For finite $N$ and $L$, a perfect agreement between the discrete student and the teacher is obtained for $\alpha \propto \sqrt{L \ln(NL)}$. A crossover to the generalization error $\propto 1/\alpha$, characterized continuous weights with binary output, is obtained for synaptic depth $L > O(\sqrt{N})$.Comment: 10 pages, 5 figs., submitted to PR