Contrary Voices: Heine, Holderlin, and Goethe in the music of Hanns Eisler

Contrary Voices examines composer Hanns Eisler’s settings of nineteenth-century poetry under changing political pressures from 1925 to 1962. The poets’ ideologically fraught reception histories, both under Nazism and in East Germany, led Eisler to intervene in this reception and voice dissent by radically fragmenting the texts. His musical settings both absorb and disturb the charisma of nineteenth-century sound materials, through formal parody, dissonance, and interruption. Eisler’s montage-like work foregrounds the difficult position of a modernist artist speaking both to and against political demands placed on art. Often the very charisma the composer seeks to expose for its power to sway the body politic exerts a force of its own. At the same time, his text-settings resist ideological rigidity in their polyphonic play. A dialogic approach to musical adaptation shows that, as Eisler seeks to resignify Heine’s problematic status in the Weimar Republic, Hölderlin’s appropriation under Nazism, and Goethe’s status as a nationalist symbol in the nascent German Democratic Republic, his music invests these poetic voices with surprising fragility and multivalence. It also destabilizes received gender tropes, in the masculine vulnerability of Eisler’s Heine choruses from 1925 and in the androgynous voices of his 1940s Hölderlin exile songs and later Goethe settings. Cross-reading the texts after hearing such musical treatment illuminates faultlines and complexities less obvious in text-only analysis. Ultimately Eisler’s music translates canonical material into a form as paradoxically faithful as it is violently fragmented.

Nested dictionary learning for hierarchical organization of imagery and text

A tree-based dictionary learning model is developed for joint analysis of imagery and associated text. The dictionary learning may be applied directly to the imagery from patches, or to general feature vectors extracted from patches or superpixels (using any existing method for image feature extraction). Each image is associated with a path through the tree (from root to a leaf), and each of the multiple patches in a given image is associated with one node in that path. Nodes near the tree root are shared between multiple paths, representing image characteristics that are common among different types of images. Moving toward the leaves, nodes become specialized, representing details in image classes. If available, words (text) are also jointly modeled, with a path-dependent probability over words. The tree structure is inferred via a nested Dirichlet process, and a retrospective stick-breaking sampler is used to infer the tree depth and width.

A meta-analysis of the effectiveness of intelligent tutoring systems on college students' academic learning

© 2013 American Psychological Association.This meta-analysis synthesizes research on the effectiveness of intelligent tutoring systems (ITS) for college students. Thirty-five reports were found containing 39 studies assessing the effectiveness of 22 types of ITS in higher education settings. Most frequently studied were AutoTutor, Assessment and Learning in Knowledge Spaces, eXtended Tutor-Expert System, and Web Interface for Statistics Education. Major findings include (a) Overall, ITS had a moderate positive effect on college students' academic learning (g = .32 to g = .37); (b) ITS were less effective than human tutoring, but they outperformed all other instruction methods and learning activities, including traditional classroom instruction, reading printed text or computerized materials, computer-assisted instruction, laboratory or homework assignments, and no-treatment control; (c) ITS's effectiveness did not significantly differ by different ITS, subject domain, or the manner or degree of their involvement in instruction and learning; and (d) effectiveness in earlier studies appeared to be significantly greater than that in more recent studies. In addition, there is some evidence suggesting the importance of teachers and pedagogy in ITS-assisted learning.

CPAG: software for leveraging pleiotropy in GWAS to reveal similarity between human traits links plasma fatty acids and intestinal inflammation.

Meta-analyses of genome-wide association studies (GWAS) have demonstrated that the same genetic variants can be associated with multiple diseases and other complex traits. We present software called CPAG (Cross-Phenotype Analysis of GWAS) to look for similarities between 700 traits, build trees with informative clusters, and highlight underlying pathways. Clusters are consistent with pre-defined groups and literature-based validation but also reveal novel connections. We report similarity between plasma palmitoleic acid and Crohn's disease and find that specific fatty acids exacerbate enterocolitis in zebrafish. CPAG will become increasingly powerful as more genetic variants are uncovered, leading to a deeper understanding of complex traits. CPAG is freely available at www.sourceforge.net/projects/CPAG/.