Origin of time reversal symmetry breaking in Y(1-y)Ca(y)Ba(2)Cu(3)O(7-x)

We have studied the Zero Bias Conductance Peak (ZBCP) of the tunneling conductance measured on (1,1,0) oriented Y(1-y)Ca(y)Ba(2)Cu(3)O(7-x) thin films as a function of doping and of magnetic field. A spontaneous (zero field) split of the ZBCP was observed only in overdoped samples (either by O or by Ca). The magnitude of this split was found to be linear in doping. All samples exhibited a magnetic field splitting, also strongly doping dependent. The field susceptibility chi=d(delta)/dH diverges at the point at which spontaneous ZBCP splitting occurs, its inverse value, chi^(-1), following a linear doping dependence on both the underdoped and overdoped sides. We discuss these results in terms of recent theoretical models of Time Reversal Symmetry Breaking (TRSB).Comment: 5 figure

Committee-Based Sample Selection for Probabilistic Classifiers

In many real-world learning tasks, it is expensive to acquire a sufficient number of labeled examples for training. This paper investigates methods for reducing annotation cost by `sample selection'. In this approach, during training the learning program examines many unlabeled examples and selects for labeling only those that are most informative at each stage. This avoids redundantly labeling examples that contribute little new information. Our work follows on previous research on Query By Committee, extending the committee-based paradigm to the context of probabilistic classification. We describe a family of empirical methods for committee-based sample selection in probabilistic classification models, which evaluate the informativeness of an example by measuring the degree of disagreement between several model variants. These variants (the committee) are drawn randomly from a probability distribution conditioned by the training set labeled so far. The method was applied to the real-world natural language processing task of stochastic part-of-speech tagging. We find that all variants of the method achieve a significant reduction in annotation cost, although their computational efficiency differs. In particular, the simplest variant, a two member committee with no parameters to tune, gives excellent results. We also show that sample selection yields a significant reduction in the size of the model used by the tagger

Mistake-Driven Learning in Text Categorization

Learning problems in the text processing domain often map the text to a space whose dimensions are the measured features of the text, e.g., its words. Three characteristic properties of this domain are (a) very high dimensionality, (b) both the learned concepts and the instances reside very sparsely in the feature space, and (c) a high variation in the number of active features in an instance. In this work we study three mistake-driven learning algorithms for a typical task of this nature -- text categorization. We argue that these algorithms -- which categorize documents by learning a linear separator in the feature space -- have a few properties that make them ideal for this domain. We then show that a quantum leap in performance is achieved when we further modify the algorithms to better address some of the specific characteristics of the domain. In particular, we demonstrate (1) how variation in document length can be tolerated by either normalizing feature weights or by using negative weights, (2) the positive effect of applying a threshold range in training, (3) alternatives in considering feature frequency, and (4) the benefits of discarding features while training. Overall, we present an algorithm, a variation of Littlestone's Winnow, which performs significantly better than any other algorithm tested on this task using a similar feature set.Comment: 9 pages, uses aclap.st

Aerosol Effect on the Mobility of Cloud Droplets

Cloud droplet mobility is referred to here as a measure of the droplets ability to move with ambient air. We claim that an important part of the aerosol effect on convective clouds is driven by changes in droplet mobility. We show that the mass-weighted average droplet terminal velocity, defined here as the effective terminal velocity (eta) and its spread (sigma_eta) serve as direct measures of this effect. Moreover, we develop analytical estimations for eta and sigma_eta to show that changes in the relative dispersion of eta (epsilon_eta = sigma_eta/eta) can serve as a sensitive predictor of the onset of droplet-collection processes.Comment: Published in ERL; 10 pages, 4 figure