Detecting a Vector Based on Linear Measurements

We consider a situation where the state of a system is represented by a real-valued vector. Under normal circumstances, the vector is zero, while an event manifests as non-zero entries in this vector, possibly few. Our interest is in the design of algorithms that can reliably detect events (i.e., test whether the vector is zero or not) with the least amount of information. We place ourselves in a situation, now common in the signal processing literature, where information about the vector comes in the form of noisy linear measurements. We derive information bounds in an active learning setup and exhibit some simple near-optimal algorithms. In particular, our results show that the task of detection within this setting is at once much easier, simpler and different than the tasks of estimation and support recovery

AN ANALYSIS OF FIGURATIVE LANGUAGE PRESENTED AT "ST JIMMY" SONG LYRIC

This study concerned with the discussion of English song’s lyric which was sung by Green Day. This study analyzed two problems (1) What kinds of figurative language are used at “St. Jimmy” song lyric? (2) What are the meanings of those figurative languages? The purpose of the study was to answer the problems proposed. This research belonged to descriptive research design. In addition, this study used the objective approach. The object of the study was “St. Jimmy” song lyric by Green Day especially in form of figurative language and meaning of figurative language. The research finding showed that Green Day used six kinds of figurative language. More specially, he used one simile, three euphemisms, one allusion, one metonymy, one metaphor, and one irony. The song told us about the life of St. Jimmy who was an ideal punk rocker. He used figurative language in order to embellish the song and exaggerate the feeling that the composer waned to express

On the convergence of maximum variance unfolding

Maximum Variance Unfolding is one of the main methods for (nonlinear) dimensionality reduction. We study its large sample limit, providing specific rates of convergence under standard assumptions. We find that it is consistent when the underlying submanifold is isometric to a convex subset, and we provide some simple examples where it fails to be consistent