Graffiti: graph-based classification in heterogeneous networks

We address the problem of multi-label classification in heterogeneous graphs, where nodes belong to different types and different types have different sets of classification labels. We present a novel approach that aims to classify nodes based on their neighborhoods. We model the mutual influence of nodes as a random walk in which the random surfer aims at distributing class labels to nodes while walking through the graph. When viewing class labels as “colors”, the random surfer is essentially spraying different node types with different color palettes; hence the name Graffiti of our method. In contrast to previous work on topic-based random surfer models, our approach captures and exploits the mutual influence of nodes of the same type based on their connections to nodes of other types. We show important properties of our algorithm such as convergence and scalability. We also confirm the practical viability of Graffiti by an experimental study on subsets of the popular social networks Flickr and LibraryThing. We demonstrate the superiority of our approach by comparing it to three other state-of-the-art techniques for graph-based classification.     

Self-assembled multicompartment liquid crystalline lipid carriers for protein, peptide, and nucleic acid drug delivery

Lipids and lipopolymers self-assembled into biocompatible nano- and mesostructured functional materials offer many potential applications in medicine and diagnostics. In this Account, we demonstrate how high-resolution structural investigations of bicontinuous cubic templates made from lyotropic thermosensitive liquid-crystalline (LC) materials have initiated the development of innovative lipidopolymeric self-assembled nanocarriers. Such structures have tunable nanochannel sizes, morphologies, and hierarchical inner organizations and provide potential vehicles for the predictable loading and release of therapeutic proteins, peptides, or nucleic acids. This Account shows that structural studies of swelling of bicontinuous cubic lipid/water phases are essential for overcoming the nanoscale constraints for encapsulation of large therapeutic molecules in multicompartment lipid carriers. For the systems described here, we have employed time-resolved small-angle X-ray scattering (SAXS) and high-resolution freeze-fracture electronic microscopy (FF-EM) to study the morphology and the dynamic topological transitions of these nanostructured multicomponent amphiphilic assemblies. Quasi-elastic light scattering and circular dichroism spectroscopy can provide additional information at the nanoscale about the behavior of lipid/protein self-assemblies under conditions that approximate physiological hydration. We wanted to generalize these findings to control the stability and the hydration of the water nanochannels in liquid-crystalline lipid nanovehicles and confine therapeutic biomolecules within these structures. Therefore we analyzed the influence of amphiphilic and soluble additives (e.g. poly(ethylene glycol)monooleate (MO-PEG), octyl glucoside (OG), proteins) on the nanochannels' size in a diamond (D)-type bicontinuous cubic phase of the lipid glycerol monooleate (MO). At body temperature, we can stabilize long-living swollen states, corresponding to a diamond cubic phase with large water channels. Time-resolved X-ray diffraction (XRD) scans allowed us to detect metastable intermediate and coexisting structures and monitor the temperature-induced phase sequences of mixed systems containing glycerol monooleate, a soluble protein macromolecule, and an interfacial curvature modulating agent. These observed states correspond to the stages of the growth of the nanofluidic channel network. With the application of a thermal stimulus, the system becomes progressively more ordered into a double-diamond cubic lattice formed by a bicontinuous lipid membrane. High-resolution freeze-fracture electronic microscopy indicates that nanodomains are induced by the inclusion of proteins into nanopockets of the supramolecular cubosomic assemblies. These results contribute to the understanding of the structure and dynamics of functionalized self-assembled lipid nanosystems during stimuli-triggered LC phase transformations.     

Mobility Tracking in Cellular Networks Using Particle Filtering

Mobility tracking based on data from wireless cellular networks is a key challenge that has been recently investigated both from a theoretical and practical point of view. This paper proposes Monte Carlo techniques for mobility tracking in wireless communication networks by means of received signal strength indications. These techniques allow for accurate estimation of mobile station's (MS) position and speed. The command process of the MS is represented by a first-order Markov model which can take values from a finite set of acceleration levels. The wide range of acceleration changes is covered by a set of preliminary determined acceleration values. A particle filter and a Rao-Blackwellised particle filter are proposed and their performance is evaluated both over synthetic and real data. A comparison with an extended Kalman filter (EKF) is performed with respect to accuracy and computational complexity. With a small number of particles the RBPF gives more accurate results than the PF and the EKF. A posterior Cramer Rao lower bound (PCRLB) is calculated and it is compared with the filters' root- mean-square error performance.     

Chloroform-extractable material from bituminous coals

Material soluble in chloroform has been extracted from bright bituminous coals of two different ranks

1. (1) following preheating to the temperatures of maximal plasticity;

2. (2) after treatment with sodium in liquid ammonia;

3. (3) by fractionating the pyridine extracts;

4. (4) after preliminary acetylation with acetic anhydride in the presence of pyridine.

The extracts were characterized by elemental analysis, average molecular weights, and n.m.r. and i.r. spectroscopy. The spectroscopic data afforded quantitative information on the distribution of hydrogen between various structural groups and some statistically averaged structural parameters. The results are discussed with regard to the mechanism of formation of the chloroform-extractable material during the various preliminary treatments of the coals.     

Density estimates on the unit simplex and calculation of the mode of a sample

This paper addresses reliable and efficient calculation of the mode of a multivariate sample, which is a classical fusion function. In particular, we focus on the inputs given on the unit simplex, when aggregating elements of Atanassov intuitionistic fuzzy sets, interval-valued fuzzy sets and their extensions, as well as compositional data. We outline the use of a specially designed 2-additive fuzzy measures and the Choquet integral for the purposes of reducing computational complexity in higher dimensions. We present computational analysis and benchmark four different methods of density-based mode estimation.     

Special Issue on Deep Learning for Robotic Vision

International Journal of Computer Vision -