ECOC-DRF: Discriminative random fields based on error correcting output codes

We present ECOC-DRF, a framework where potential functions for Discriminative Random Fields are formulated as an ensemble of classifiers. We introduce the label trick, a technique to express transitions in the pairwise potential as meta-classes. This allows to independently learn any possible transition between labels without assuming any pre-defined model. The Error Correcting Output Codes matrix is used as ensemble framework for the combination of margin classifiers. We apply ECOC-DRF to a large set of classification problems, covering synthetic, natural and medical images for binary and multi-class cases, outperforming state-of-the art in almost all the experiments.     

Distance learning for similarity estimation

In this paper, we present a general guideline to find a better distance measure for similarity estimation based on statistical analysis of distribution models and distance functions. A new set of distance measures are derived from the harmonic distance, the geometric distance, and their generalized variants according to the Maximum Likelihood theory. These measures can provide a more accurate feature model than the classical Euclidean and Manhattan distances. We also find that the feature elements are often from heterogeneous sources that may have different influence on similarity estimation. Therefore, the assumption of single isotropic distribution model is often inappropriate. To alleviate this problem, we use a boosted distance measure framework that finds multiple distance measures which fit the distribution of selected feature elements best for accurate similarity estimation. The new distance measures for similarity estimation are tested on two applications: stereo matching and motion tracking in video sequences. The performance of boosted distance measure is further evaluated on several benchmark data sets from the UCI repository and two image retrieval applications. In all the experiments, robust results are obtained based on the proposed methods.     

An analysis of COMET and MAGIC using the standard reference model for intelligent multimedia presentation systems

We provide an overview of two multimedia generation systems developed at Columbia University, COMET and MAGIC, and analyze them using the standard reference model for intelligent multimedia presentation systems. COMET (Coordinated Multimedia Explanation Testbed) emphasizes the coordinated generation of text and 3D graphics in a military maintenance and repair documentation domain. It features support for automated determination of what information to present, assignment of information to appropriate media and generation of textual cross-references to the structure and content of generated graphics, including the automated redesign of graphics to make possible better cross-references. MAGIC (Multimedia Abstract Generation for Intensive Care) generates presentations that use temporal media, including speech and animation, to explain the status of hospital patients. It extends the media coordination approach used in COMET to include a multi-stage negotiation process that solves temporal constraints imposed by individual media generators to determine when and for how long actions should occur in each medium.     

Size-Dependent Cellular Uptake of RGD Peptides

Monomeric RGD peptides show unspecific fluid-phase uptake in cells, whereas multimeric RGD peptides are thought to be internalized by integrin-mediated endocytosis. However, a potential correlation between uptake mechanism and molecular mass has been neglected so far. A dual derivatization of peptide c(RGDw(7Br)K) was performed to investigate this. A fluorescent probe was installed by chemoselective Suzuki–Miyaura cross-coupling of the 7-bromotryptophan and a poly(ethylene glycol) (PEG) linker was attached to the lysine residue. Flow cytometry and live cell imaging confirmed unspecific uptake of the small, non-PEGylated peptide, whereas the PEG₅₀₀₀ peptide conjugate unveiled a selective internalization by M21 cells overexpressing α_vβ₃ and no uptake in α_v-deficient M21L cells.     

Traffic sign recognition system with β -correction

Traffic sign classification represents a classical application of multi-object recognition processing in uncontrolled adverse environments. Lack of visibility, illumination changes, and partial occlusions are just a few problems. In this paper, we introduce a novel system for multi-class classification of traffic signs based on error correcting output codes (ECOC). ECOC is based on an ensemble of binary classifiers that are trained on bi-partition of classes. We classify a wide set of traffic signs types using robust error correcting codings. Moreover, we introduce the novel β-correction decoding strategy that outperforms the state-of-the-art decoding techniques, classifying a high number of classes with great success.     

Lignocellulosic Biofuels: Phase Separation during Catalytic Hydrodeoxygenation of Liquid Phase Pyrolysis Oil

This paper contributes to the understanding of liquid phase pyrolysis (LPP) oil upgrading. The subject of discussion is hydrodeoxygenation (HDO). A three-stage hydrotreatment of liquid phase pyrolysis oil is described. It was found that during the initial heating stage conditions no HDO oil was produced. The HDO oil was formed during the main heating stage. During the initial heating stage, the oxygen content and the average molecular weight remained relatively constant. In the main heating stage the oxygen content decreased from 40 wt.% to 24 wt.% and the average molecular weight also decreases from 630 to 570 g/mol. Finally in the isothermal stage HDO oil was formed, indicated by a drop in oxygen content.     

Psychoanalysis: Then and now.

The panel "Learning Psychoanalysis, Then and Now" was convened to compare psychoanalytic training in the 1960s to contemporary training. More specifically, the panelists addressed John Ross's contention that rigid, controlling, and sadomasochistic features of psychoanalytic institutes encourage deferential submissiveness by casting dissent as a form of acting out. Panelists used their own experiences in training and at their institutes to discuss these issues. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Intravascular Ultrasound Tissue Characterization with Sub-class Error-Correcting Output Codes

Intravascular ultrasound (IVUS) represents a powerful imaging technique to explore coronary vessels and to study their morphology and histologic properties. In this paper, we characterize different tissues based on radial frequency, texture-based, and combined features. To deal with the classification of multiple tissues, we require the use of robust multi-class learning techniques. In this sense, error-correcting output codes (ECOC) show to robustly combine binary classifiers to solve multi-class problems. In this context, we propose a strategy to model multi-class classification tasks using sub-classes information in the ECOC framework. The new strategy splits the classes into different sub-sets according to the applied base classifier. Complex IVUS data sets containing overlapping data are learnt by splitting the original set of classes into sub-classes, and embedding the binary problems in a problem-dependent ECOC design. The method automatically characterizes different tissues, showing performance improvements over the state-of-the-art ECOC techniques for different base classifiers. Furthermore, the combination of RF and texture-based features also shows improvements over the state-of-the-art approaches.