Investment & Strategy at Morse Cutting Tool

[Excerpt] Local #277 of the United Electrical, Radio and Machine Workers of America contracted with the ICA for a preliminary assessment of the long term viability of Morse Cutting Tools. The union had become alarmed by declining employment at Morse and by Morse management\u27s statements regarding the company\u27s inadequate profitability and shrinking market share. Of even greater concern was the threat that the conglomerate which owns Morse, Gulf+Western (G+W), might close the New Bedford plant. We were asked to examine Morse\u27s position in the cutting tool business with particular attention to the adequacy of G+W\u27s investment in plant and equipment and of Morse\u27s management strategy

Robust Independent Component Analysis via Minimum Divergence Estimation

Independent component analysis (ICA) has been shown to be useful in many applications. However, most ICA methods are sensitive to data contamination and outliers. In this article we introduce a general minimum U-divergence framework for ICA, which covers some standard ICA methods as special cases. Within the U-family we further focus on the gamma-divergence due to its desirable property of super robustness, which gives the proposed method gamma-ICA. Statistical properties and technical conditions for the consistency of gamma-ICA are rigorously studied. In the limiting case, it leads to a necessary and sufficient condition for the consistency of MLE-ICA. This necessary and sufficient condition is weaker than the condition known in the literature. Since the parameter of interest in ICA is an orthogonal matrix, a geometrical algorithm based on gradient flows on special orthogonal group is introduced to implement gamma-ICA. Furthermore, a data-driven selection for the gamma value, which is critical to the achievement of gamma-ICA, is developed. The performance, especially the robustness, of gamma-ICA in comparison with standard ICA methods is demonstrated through experimental studies using simulated data and image data.Comment: 7 figure

Cytoplasmic islet cell antibodies recognize distinct islet antigens in IDDM but not in stiff man syndrome

Cytoplasmic islet cell antibodies are well-established predictive markers of IDDM. Although target molecules of ICA have been suggested to be gangliosides, human monoclonal ICA of the immunoglobulin G class (MICA 1-6) produced from a patient with newly diagnosed IDDM recognized glutamate decarboxylase as a target antigen. Here we analyzed the possible heterogeneity of target antigens of ICA by subtracting the GAD-specific ICA staining from total ICA staining of sera. This was achieved 1) by preabsorption of ICA+ sera with recombinant GAD65 and/or GAD67 expressed in a baculovirus system and 2) by ICA analysis of sera on mouse pancreas, as GAD antibodies do not stain mouse islets in the immunofluorescence test. We show that 24 of 25 sera from newly diagnosed patients with IDDM recognize islet antigens besides GAD. In contrast, GAD was the only islet antigen recognized by ICA from 7 sera from patients with stiff man syndrome. Two of these sera, however, recognized antigens besides GAD in Purkinje cells. In patients with IDDM, non-GAD ICA were diverse. One group, found in 64% of the sera, stained human and mouse islets, whereas the other group of non-GAD ICA was human specific. Therefore, mouse islets distinguish two groups of non-GAD ICA and lack additional target epitopes of ICA besides GAD. Longitudinal analysis of 6 sera from nondiabetic ICA+ individuals revealed that mouse-reactive ICA may appear closer to clinical onset of IDDM in some individuals

CanICA: Model-based extraction of reproducible group-level ICA patterns from fMRI time series

Spatial Independent Component Analysis (ICA) is an increasingly used data-driven method to analyze functional Magnetic Resonance Imaging (fMRI) data. To date, it has been used to extract meaningful patterns without prior information. However, ICA is not robust to mild data variation and remains a parameter-sensitive algorithm. The validity of the extracted patterns is hard to establish, as well as the significance of differences between patterns extracted from different groups of subjects. We start from a generative model of the fMRI group data to introduce a probabilistic ICA pattern-extraction algorithm, called CanICA (Canonical ICA). Thanks to an explicit noise model and canonical correlation analysis, our method is auto-calibrated and identifies the group-reproducible data subspace before performing ICA. We compare our method to state-of-the-art multi-subject fMRI ICA methods and show that the features extracted are more reproducible

Novel hybrid extraction systems for fetal heart rate variability monitoring based on non-invasive fetal electrocardiogram

This study focuses on the design, implementation and subsequent verification of a new type of hybrid extraction system for noninvasive fetal electrocardiogram (NI-fECG) processing. The system designed combines the advantages of individual adaptive and non-adaptive algorithms. The pilot study reviews two innovative hybrid systems called ICA-ANFIS-WT and ICA-RLS-WT. This is a combination of independent component analysis (ICA), adaptive neuro-fuzzy inference system (ANFIS) algorithm or recursive least squares (RLS) algorithm and wavelet transform (WT) algorithm. The study was conducted on clinical practice data (extended ADFECGDB database and Physionet Challenge 2013 database) from the perspective of non-invasive fetal heart rate variability monitoring based on the determination of the overall probability of correct detection (ACC), sensitivity (SE), positive predictive value (PPV) and harmonic mean between SE and PPV (F1). System functionality was verified against a relevant reference obtained by an invasive way using a scalp electrode (ADFECGDB database), or relevant reference obtained by annotations (Physionet Challenge 2013 database). The study showed that ICA-RLS-WT hybrid system achieve better results than ICA-ANFIS-WT. During experiment on ADFECGDB database, the ICA-RLS-WT hybrid system reached ACC > 80 % on 9 recordings out of 12 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 6 recordings out of 12. During experiment on Physionet Challenge 2013 database the ICA-RLS-WT hybrid system reached ACC > 80 % on 13 recordings out of 25 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 7 recordings out of 25. Both hybrid systems achieve provably better results than the individual algorithms tested in previous studies.Web of Science713178413175