Crossmodal content binding in information-processing architectures

Operating in a physical context, an intelligent robot faces two fundamental problems. First, it needs to combine information from its different sensors to form a representation of the environment that is more complete than any of its sensors on its own could provide. Second, it needs to combine high-level representations (such as those for planning and dialogue) with its sensory information, to ensure that the interpretations of these symbolic representations are grounded in the situated context. Previous approaches to this problem have used techniques such as (low-level) information fusion, ontological reasoning, and (high-level) concept learning. This paper presents a framework in which these, and other approaches, can be combined to form a shared representation of the current state of the robot in relation to its environment and other agents. Preliminary results from an implemented system are presented to illustrate how the framework supports behaviours commonly required of an intelligent robot

Equilibrium and stability of neutrino lumps as TOV solutions

We report about stability conditions for static, spherically symmetric objects that share the essential features of mass varying neutrinos in cosmological scenarios. Compact structures of particles with variable mass are held together preponderantly by an attractive force mediated by a background scalar field. Their corresponding conditions for equilibrium and stability are given in terms of the ratio between the total mass-energy and the spherical lump radius, $M/R$. We show that the mass varying mechanism leading to lump formation can modify the cosmological predictions for the cosmological neutrino mass limits. Our study comprises Tolman-Oppenheimer-Volkoff solutions of relativistic objects with non-uniform energy densities. The results leave open some questions concerning stable regular solutions that, to an external observer, very closely reproduce the preliminary conditions to form Schwarzschild black holes.Comment: 20 pages, 5 figure

Scenarios and research issues for a network of information

This paper describes ideas and items of work within the framework of the EU-funded 4WARD project. We present scenarios where the current host-centric approach to infor- mation storage and retrieval is ill-suited for and explain how a new networking paradigm emerges, by adopting the information-centric network architecture approach, which we call Network of Information (NetInf). NetInf capital- izes on a proposed identifier/locator split and allows users to create, distribute, and retrieve information using a com- mon infrastructure without tying data to particular hosts. NetInf introduces the concepts of information and data ob- jects. Data objects correspond to the particular bits and bytes of a digital object, such as text file, a specific encod- ing of a song or a video. Information objects can be used to identify other objects irrespective of their particular dig- ital representation. After discussing the benefits of such an indirection, we consider the impact of NetInf with respect to naming and governance in the Future Internet. Finally, we provide an outlook on the research scope of NetInf along with items for future work

On Classification with Bags, Groups and Sets

Many classification problems can be difficult to formulate directly in terms of the traditional supervised setting, where both training and test samples are individual feature vectors. There are cases in which samples are better described by sets of feature vectors, that labels are only available for sets rather than individual samples, or, if individual labels are available, that these are not independent. To better deal with such problems, several extensions of supervised learning have been proposed, where either training and/or test objects are sets of feature vectors. However, having been proposed rather independently of each other, their mutual similarities and differences have hitherto not been mapped out. In this work, we provide an overview of such learning scenarios, propose a taxonomy to illustrate the relationships between them, and discuss directions for further research in these areas

Low-Mass Binary Induced Outflows from Asymptotic Giant Branch Stars

A significant fraction of planetary nebulae (PNe) and proto-planetary nebulae (PPNe) exhibit aspherical, axisymmetric structures, many of which are highly collimated. The origin of these structures is not entirely understood, however recent evidence suggests that many observed PNe harbor binary systems, which may play a role in their shaping. In an effort to understand how binaries may produce such asymmetries, we study the effect of low-mass (< 0.3 M_sun) companions (planets, brown dwarfs and low-mass main sequence stars) embedded into the envelope of a 3.0 M_sun star during three epochs of its evolution (Red Giant Branch, Asymptotic Giant Branch (AGB), interpulse AGB). We find that common envelope evolution can lead to three qualitatively different consequences: (i) direct ejection of envelope material resulting in a predominately equatorial outflow, (ii) spin-up of the envelope resulting in the possibility of powering an explosive dynamo driven jet and (iii) tidal shredding of the companion into a disc which facilitates a disc driven jet. We study how these features depend on the secondary's mass and discuss observational consequences.Comment: 24 pages, 6 figures, submitted to MNRA

Model mass spectrometric study of competitive interactions of antimicrobial bisquaternary ammonium drugs and aspirin with membrane phospholipids

The aim of the study is to reveal molecular mechanisms of possible activity modulation of antimicrobial bis-quaternary ammonium compounds (BQAC) and aspirin (ASP) through noncovalent competitive complexation under their combined introduction into the model systems with membrane phospholipids. Methods. Binary and triple systems containing either decamethoxinum or ethonium, or thionium and aspirin, as well as dipalmitoyl-phosphatidylcholine (DPPC) have been investigated by electrospray ionization mass spectrometry. Results. Basing on the analysis of associates recorded in the mass spectra, the types of nonocovalent complexes formed in the systems studied were determined and the supposed role of the complexation in the BQAC and ASP activity modulation was discussed. The formation of associates of BQAC dications with ASP anion is considered as one of the possible ways of deactivation of ionic forms of the medications. The formation of stable complexes of BQAC with DPPC and ASP with DPPC in binary systems as well as the complexes distribution in triple-components systems BQAC:ASP:DPPC point to the existence of competition between drugs of these two types for the binding to DPPC. Conclusions. The results obtained point to the competitive complexation in the model molecular systems containing the BQAC, aspirin and membrane phospholipids. The observed phenomenon testifies to the possibility of modulating the activity of bisquaternary antimicrobial agents and aspirin under their combined usage, due to the competition between the drugs for binding to the target membrane phospholipid molecules and also due to the formation of stable noncovalent complexes between BQAC and ASP

Solution Path Clustering with Adaptive Concave Penalty

Fast accumulation of large amounts of complex data has created a need for more sophisticated statistical methodologies to discover interesting patterns and better extract information from these data. The large scale of the data often results in challenging high-dimensional estimation problems where only a minority of the data shows specific grouping patterns. To address these emerging challenges, we develop a new clustering methodology that introduces the idea of a regularization path into unsupervised learning. A regularization path for a clustering problem is created by varying the degree of sparsity constraint that is imposed on the differences between objects via the minimax concave penalty with adaptive tuning parameters. Instead of providing a single solution represented by a cluster assignment for each object, the method produces a short sequence of solutions that determines not only the cluster assignment but also a corresponding number of clusters for each solution. The optimization of the penalized loss function is carried out through an MM algorithm with block coordinate descent. The advantages of this clustering algorithm compared to other existing methods are as follows: it does not require the input of the number of clusters; it is capable of simultaneously separating irrelevant or noisy observations that show no grouping pattern, which can greatly improve data interpretation; it is a general methodology that can be applied to many clustering problems. We test this method on various simulated datasets and on gene expression data, where it shows better or competitive performance compared against several clustering methods.Comment: 36 page