Descriptio rheumatismi acuti, et dilucidatio ducentorum et quinquaginta aphorismorum Hippocratis ad rheumatismum tum acutum tum chronicum, item ad phrenitidem, ad paraphrenitidem, ad pleuritidem, peripneumoniam, pleuropneumoniam et anginam, duobus libellis priori anno a me descriptas, pertinentium data /

Mode of access: Internet.

Io. Iac. Baieri ... Introductio in medicinam forensem et responsa eiusdem argumenti tam ordinis sui nomine quam propria autoritate data ... proponuntur a Ferd. Iac. Baiero.

Mode of access: Internet.

Ad Ephesinum Concilium Variorum Patrum Epistolae : ex manu-scripto Cassinensis Bibliothecae Codice desumptae : item ex Vaticanae Bibliothecae manu-scripto ... nunc primum in publicam lucem data /

Mode of access: Internet.

Memorie presentate da piu' cardinali, arcivescovi e vescovi a sua altezza reale il duca d'Orleans reggente di Francia per impegnarlo a punire, e frenare gli attentati de' Quesnellisti dalla lingua francese nella italiana tradotte ed illustrate con prefazione, ragguaglio della storia del libro delle Riflessioni morali di Quesnello, e note interessanti, contro il Testimonio, e trionfo della dottrina di Pistoja e il discorso della monarchia universale de Papi stampato senza data di luogo nel MDLXXXIX.

Ref. en ICCU.

Documenta divinae sapientiae pro digno principe, Salomoni filio regis Davidis olim data; nunc paraphrasi elegiacà explicata ... Ludovico Antonio Jacobo ... : annum complenti XV ... /

Mode of access: Internet.

Breve ragguaglio della vita del venerabile P. Francesco Geronimo della compagnia di Giesu : ricavato dalla vita di lui piu diffusamente scritta, e data alla luce l'anno 1747 ...

Mode of access: Internet.

La reggia in trionfo per l'acclamazione, e coronazione della sacra real maesta di Carlo infante di Spagna, ... ordinata dall'eccellentissimo Senato palermitano ... data in luce in tempo dell'eccellentissimo Senato ... e descritta da don Pietro La Placa cancelliere della citta

Palau y Dulcet, 1948, VII, 131416.

Submodular Optimization and Data Processing

Thesis (Ph.D.)--University of Washington, 2016-06

Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images

Mixture models implemented via the expectation-maximization (EM) algorithm are being increasingly used in a wide range of problems in pattern recognition such as image segmentation. However, the EM algorithm requires considerable computational time in its application to huge data sets such as a three-dimensional magnetic resonance (MR) image of over 10 million voxels. Recently, it was shown that a sparse, incremental version of the EM algorithm could improve its rate of convergence. In this paper, we show how this modified EM algorithm can be speeded up further by adopting a multiresolution kd-tree structure in performing the E-step. The proposed algorithm outperforms some other variants of the EM algorithm for segmenting MR images of the human brain. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Using the EM Algorithm to Train Neural Networks: Misconceptions and a New Algorithm for Multiclass Classification

The expectation-maximization (EM) algorithm has been of considerable interest in recent years as the basis for various algorithms in application areas of neural networks such as pattern recognition. However, there exists some misconceptions concerning its application to neural networks. In this paper, we clarify these misconceptions and consider how the EM algorithm can be adopted to train multilayer perceptron (MLP) and mixture of experts (ME) networks in applications to multiclass classification. We identify some situations where the application of the EM algorithm to train MLP networks may be of limited value and discuss some ways of handling the difficulties. For ME networks, it is reported in the literature that networks trained by the EM algorithm using iteratively reweighted least squares (IRLS) algorithm in the inner loop of the M-step, often performed poorly in multiclass classification. However, we found that the convergence of the IRLS algorithm is stable and that the log likelihood is monotonic increasing when a learning rate smaller than one is adopted. Also, we propose the use of an expectation-conditional maximization (ECM) algorithm to train ME networks. Its performance is demonstrated to be superior to the IRLS algorithm on some simulated and real data sets.

Deriving matrix of peptide-MHC interactions in diabetic mouse by genetic algorithm

Finding motifs that can elucidate rules that govern peptide binding to medically important receptors is important for screening targets for drugs and vaccines. This paper focuses on elucidation of peptide binding to I-A(g7) molecule of the non-obese diabetic (NOD) mouse - an animal model for insulin-dependent diabetes mellitus (IDDM). A number of proposed motifs that describe peptide binding to I-A(g7) have been proposed. These motifs results from independent experimental studies carried out on small data sets. Testing with multiple data sets showed that each of the motifs at best describes only a subset of the solution space, and these motifs therefore lack generalization ability. This study focuses on seeking a motif with higher generalization ability so that it can predict binders in all A(g7) data sets with high accuracy. A binding score matrix representing peptide binding motif to A(g7) was derived using genetic algorithm (GA). The evolved score matrix significantly outperformed previously reported

GANN: Genetic algorithm neural networks for the detection of conserved combinations of features in DNA

Background: The multitude of motif detection algorithms developed to date have largely focused on the detection of patterns in primary sequence. Since sequence-dependent DNA structure and flexibility may also play a role in protein-DNA interactions, the simultaneous exploration of sequence-and structure-based hypotheses about the composition of binding sites and the ordering of features in a regulatory region should be considered as well. The consideration of structural features requires the development of new detection tools that can deal with data types other than primary sequence. Results: GANN ( available at http://bioinformatics.org.au/gann) is a machine learning tool for the detection of conserved features in DNA. The software suite contains programs to extract different regions of genomic DNA from flat files and convert these sequences to indices that reflect sequence and structural composition or the presence of specific protein binding sites. The machine learning component allows the classification of different types of sequences based on subsamples of these indices, and can identify the best combinations of indices and machine learning architecture for sequence discrimination. Another key feature of GANN is the replicated splitting of data into training and test sets, and the implementation of negative controls. In validation experiments, GANN successfully merged important sequence and structural features to yield good predictive models for synthetic and real regulatory regions. Conclusion: GANN is a flexible tool that can search through large sets of sequence and structural feature combinations to identify those that best characterize a set of sequences.

A new algorithm for the detection of intercluster galaxy filaments using galaxy orientation alignments

We present a new algorithm for detecting intercluster galaxy filaments based upon the assumption that the orientations of constituent galaxies along such filaments are non-isotropic. We apply the algorithm to the 2dF Galaxy Redshift Survey catalogue and find that it readily detects many straight filaments between close cluster pairs. At large intercluster separations (> 15 h(-1) Mpc), we find that the detection efficiency falls quickly, as it also does with more complex filament morphologies. We explore the underlying assumptions and suggest that it is only in the case of close cluster pairs that we can expect galaxy orientations to be significantly correlated with filament direction.

Normalized Gaussian Networks with Mixed Feature Data

With mixed feature data, problems are induced in modeling the gating network of normalized Gaussian (NG) networks as the assumption of multivariate Gaussian becomes invalid. In this paper, we propose an independence model to handle mixed feature data within the framework of NG networks. The method is illustrated using a real example of breast cancer data.