Interaction in Quantum Communication

In some scenarios there are ways of conveying information with many fewer, even exponentially fewer, qubits than possible classically. Moreover, some of these methods have a very simple structure--they involve only few message exchanges between the communicating parties. It is therefore natural to ask whether every classical protocol may be transformed to a ``simpler'' quantum protocol--one that has similar efficiency, but uses fewer message exchanges. We show that for any constant k, there is a problem such that its k+1 message classical communication complexity is exponentially smaller than its k message quantum communication complexity. This, in particular, proves a round hierarchy theorem for quantum communication complexity, and implies, via a simple reduction, an Omega(N^{1/k}) lower bound for k message quantum protocols for Set Disjointness for constant k. Enroute, we prove information-theoretic lemmas, and define a related measure of correlation, the informational distance, that we believe may be of significance in other contexts as well.Comment: 35 pages. Uses IEEEtran.cls, IEEEbib.bst. Submitted to IEEE Transactions on Information Theory. Strengthens results in quant-ph/0005106, quant-ph/0004100 and an earlier version presented in STOC 200

Measuring Shared Information and Coordinated Activity in Neuronal Networks

Most nervous systems encode information about stimuli in the responding activity of large neuronal networks. This activity often manifests itself as dynamically coordinated sequences of action potentials. Since multiple electrode recordings are now a standard tool in neuroscience research, it is important to have a measure of such network-wide behavioral coordination and information sharing, applicable to multiple neural spike train data. We propose a new statistic, informational coherence, which measures how much better one unit can be predicted by knowing the dynamical state of another. We argue informational coherence is a measure of association and shared information which is superior to traditional pairwise measures of synchronization and correlation. To find the dynamical states, we use a recently-introduced algorithm which reconstructs effective state spaces from stochastic time series. We then extend the pairwise measure to a multivariate analysis of the network by estimating the network multi-information. We illustrate our method by testing it on a detailed model of the transition from gamma to beta rhythms.Comment: 8 pages, 6 figure

Religiosity, identity, and depression in late adolescence: A longitudinal study

In this study, longitudinal associations among religiosity, identity style, identity commitment, and depression were examined in a sample of late adolescents. Online survey data were collected in two waves with an approximate six-week interval. Correlations demonstrated that high levels of negative aspects of religiosity, such as negative religious coping, predicted high levels of depression. Other aspects of religiosity, such as positive religious coping, did not predict depression. In addition, high levels of diffuse-avoidant identity style predicted high levels of depression, and high levels of identity commitment predicted low levels of depression. However, when a regression was performed with all the predictors of wave 2 depression and controlling for depression at wave 1, the predictors were no longer significant. Associations between identity and religiosity were also examined

On Measure Transformed Canonical Correlation Analysis

In this paper linear canonical correlation analysis (LCCA) is generalized by applying a structured transform to the joint probability distribution of the considered pair of random vectors, i.e., a transformation of the joint probability measure defined on their joint observation space. This framework, called measure transformed canonical correlation analysis (MTCCA), applies LCCA to the data after transformation of the joint probability measure. We show that judicious choice of the transform leads to a modified canonical correlation analysis, which, in contrast to LCCA, is capable of detecting non-linear relationships between the considered pair of random vectors. Unlike kernel canonical correlation analysis, where the transformation is applied to the random vectors, in MTCCA the transformation is applied to their joint probability distribution. This results in performance advantages and reduced implementation complexity. The proposed approach is illustrated for graphical model selection in simulated data having non-linear dependencies, and for measuring long-term associations between companies traded in the NASDAQ and NYSE stock markets

Cross-listing, price discovery and the informativeness of the trading process

This paper analyzes the price discovery process of a set of Spanish stocks cross-listed at the NYSE. Our methodology distinguishes between two sources of information asymmetries. Market-specific information that is revealed through the trading process and public disclosures simultaneously revealed to both markets but subject to informed judgments. We compute the information share of the Spanish and U.S. trading activity during the daily 2-hour overlapping interval. Empirical results show that the NYSE contribution to the price discovery process is not negligible. But the NYSE information is basically trade-unrelated

Herd behavior and contagion in financial markets

Imitative behavior and contagion are well-documented regularities of financial markets. We study whether they can occur in a two-asset economy where rational agents trade sequentially. When traders have gains from trade, informational cascades arise and prices fail to aggregate information dispersed among traders. During a cascade all informed traders with the same preferences choose the same action, i.e., they herd. Moreover, herd behavior can generate financial contagion. Informational cascades and herds can spill over from one asset to the other, pushing the price of the other asset far from its fundamental value

The effect of informational load on disfluencies in interpreting: a corpus-based regression analysis

This article attempts to measure the cognitive or informational load in interpreting by modelling the occurrence rate of the speech disfluency uh(m). In a corpus of 107 interpreted and 240 non-interpreted texts, informational load is operationalized in terms of four measures: delivery rate, lexical density, percentage of numerals, and average sentence length. The occurrence rate of the indicated speech disfluency was modelled using a rate model. Interpreted texts are analyzed based on the interpreter's output and compared with the input of non-interpreted texts, and measure the effect of source text features. The results demonstrate that interpreters produce significantly more uh(m) s than non-interpreters and that this difference is mainly due to the effect of lexical density on the output side. The main source predictor of uh(m) s in the target text was shown to be the delivery rate of the source text. On a more general level of significance, the second analysis also revealed an increasing effect of the numerals in the source texts and a decreasing effect of the numerals in the target texts