Privacy-preserving boosting

We describe two algorithms, BiBoost (Bipartite Boosting) and MultBoost (Multiparty Boosting), that allow two or more participants to construct a boosting classifier without explicitly sharing their data sets. We analyze both the computational and the security aspects of the algorithms. The algorithms inherit the excellent generalization performance of AdaBoost. Experiments indicate that the algorithms are better than AdaBoost executed separately by the participants, and that, independently of the number of participants, they perform close to AdaBoost executed using the entire data set. Responsible Editor: Charu Aggarwal.     

Logical Representation of a Conceptual Model for Spatial Data Warehouses

The MultiDimER model is a conceptual model used for representing a multidimensional view of data for Data Warehouse (DW) and On-Line Analytical Processing (OLAP) applications. This model includes a spatial extension allowing spatiality in levels, hierarchies, fact relationships, and measures. In this way decision-making users can represent in an abstract manner their analysis needs without considering complex implementation issues and spatial OLAP tools developers can have a common vision for representing spatial data in a multidimensional model. In this paper we propose the transformation of a conceptual schema based on the MultiDimER constructs to an object-relational schema. We based our mapping on the SQL:2003 and SQL/MM standards giving examples of commercial implementation using Oracle 10g with its spatial extension. Further we use spatial integrity constraints to ensure the semantic equivalence of the conceptual and logical schemas. We also show some examples of Oracle spatial functions, including aggregation functions required for the manipulation of spatial data. The described mappings to the object-relational model along with the examples using a commercial system show the feasibility of implementing spatial DWs in current commercial DBMSs. Further, using integrated architectures, where spatial and thematic data is defined within the same DBMS, facilitates the system management simplifying data definition and manipulation.

Analysis of the HLA class I associated peptide repertoire in a hepatocellular carcinoma cell line reveals tumor-specific peptides as putative targets for immunotherapy

HLA class I molecules present peptides on the cell surface to CD8(+) T cells. The repertoire of peptides that associate to class I molecules represents the cellular proteome. Therefore, cells expressing different proteomes could generate different class I-associated peptide repertoires. A large number of peptides have been sequenced from HLA class I alleles, mostly from lymphoid cells. On the other hand, T cell immunotherapy is a goal in the fight against cancer, but the identification of T cell epitopes is a laborious task. Proteomic techniques allow the definition of putative T cell epitopes by the identification of HLA natural ligands in tumor cells. In this study, we have compared the HLA class I-associated peptide repertoire from the hepatocellular carcinoma (HCC) cell line SK-Hep-1 with that previously described from lymphoid cells. The analysis of the peptide pool confirmed that, as expected, the peptides from SK-Hep-1 derive from proteins localized in the same compartments as in lymphoid cells. Within this pool, we have identified 12 HLA class I peptides derived from HCC-related proteins. This confirms that tumor cell lines could be a good source of tumor associated antigens to be used, together with MS, to define putative epitopes for cytotoxic T cells from cancer patients.     

Subacute proteome changes following traumatic injury of the developing brain: Implications for a dysregulation of neuronal migration and neurite arborization

Traumatic brain injury (TBI) is a major cause of morbidity and mortality among children and adolescents. To gain insight into developmental events influenced by TBI, we analyzed subacute mouse brain proteome changes in a percussion head trauma model at P7 ipsi- and contralateral to the site of injury. The comparison of brain proteomes of trauma mice and controls revealed reproducible changes in the intensity of 28 proteins (30 protein spots) in response to trauma. The changes detected suggest that TBI leads to apoptosis, inflammation, and oxidative stress. These changes were consistent with our results of histological and biochemical evaluation of the brains which revealed widespread apoptotic neurodegeneration, microglia activation, and increased levels of protein carbonyls. Furthermore, we detected changes in proteins involved in neuronal migration as well as axonal and dendritic growth and guidance, suggesting interference of trauma with these developmental events.     

Guidelines for the incremental identification of aspects in requirements specifications

The desired principle of separation of concerns in software development can be jeopardized by the so-called crosscutting concerns, which tend to be scattered over (and tangled with) the functionality of the modular units of a system. The correct identification of such concerns (and their encapsulation into separate artifacts) is thereby considered a way to improve software understanding and evolution. Pursuing a proper management of concerns from the requirements engineering stage can greatly benefit the entire software life-cycle. In this paper, we propose conceptual guidelines on how to perform the identification of crosscutting concerns in the process of building requirements specifications. We argue that the identification must be carried out in an incremental way, to encapsulate apart the crosscutting concerns even if they have not emerged completely yet.

Phasic norepinephrine: a neural interrupt signal for unexpected events

Extensive animal studies indicate that the neuromodulator norepinephrine plays an important role in specific aspects of vigilance, attention and learning, putatively serving as a neural interrupt or reset function. The activity of norepinephrine-releasing neurons in the locus coeruleus during attentional tasks is modulated not only by the animal's level of engagement and the sensory inputs, but also by temporally rich aspects of internal decision-making processes. Here, we propose that it is unexpected changes in the world within the context of a task that activate the noradrenergic interrupt signal. We quantify this idea in a Bayesian model of a well-studied visual discrimination task, demonstrating that the model captures a rich repertoire of noradrenergic responses at the sub-second temporal resolution.     

On a quasiorder on the class of monounary algebras

In the paper we introduce a relation on the class of monounary algebras by means of -homomorphisms. It is a quasiorder. We take a subclass of containing monounary algebras satisfying the property We characterize algebras in by the notions of a degree and properties of their -endomorphisms. We apply the results to finite monounary algebras. Supported by grant VEGA 1/0161/03     

Free binary decision diagrams for the computation of EAR n

Free binary decision diagrams (FBDDs) are graph-based data structures representing Boolean functions with the constraint (additional to binary decision diagram) that each variable is tested at most once during the computation. The function EAR_n is the following Boolean function defined for n × n Boolean matrices: EAR_n(M) = 1 iff the matrix M contains two equal adjacent rows. We prove that each FBDD computing EAR_n has size at least and we present a construction of such diagrams of size approximately .     

Progressive volume rendering of large unstructured grids

We describe a new progressive technique that allows real-time rendering of extremely large tetrahedral meshes. Our approach uses a client-server architecture to incrementally stream portions of the mesh from a server to a client which refines the quality of the approximate rendering until it converges to a full quality rendering. The results of previous steps are re-used in each subsequent refinement, thus leading to an efficient rendering. Our novel approach keeps very little geometry on the client and works by refining a set of rendered images at each step. Our interactive representation of the dataset is efficient, light-weight, and high quality. We present a framework for the exploration of large datasets stored on a remote server with a thin client that is capable of rendering and managing full quality volume visualizations.     

Interactive visual analysis of families of function graphs

The analysis and exploration of multidimensional and multivariate data is still one of the most challenging areas in the field of visualization. In this paper, we describe an approach to visual analysis of an especially challenging set of problems that exhibit a complex internal data structure. We describe the interactive visual exploration and analysis of data that includes several (usually large) families of function graphs f_i(x, t). We describe analysis procedures and practical aspects of the interactive visual analysis specific to this type of data (with emphasis on the function graph characteristic of the data). We adopted the well-proven approach of multiple, linked views with advanced interactive brushing to assess the data. Standard views such as histograms, scatterplots, and parallel coordinates are used to jointly visualize data. We support iterative visual analysis by providing means to create complex, composite brushes that span multiple views and that are constructed using different combination schemes. We demonstrate that engineering applications represent a challenging but very applicable area for visual analytics. As a case study, we describe the optimization of a fuel injection system in diesel engines of passenger cars