Detecting Multipartite Classical States and their Resemblances

We study various types of multipartite states lying near the quantum-classical boundary. The class of so-called classical states are precisely those in which each party can perform a projective measurement to identify a locally held state without disturbing the global state, a task known as non-disruptive local state identification (NDLID). We introduce a new class of states called generalized-classical states which allow for NDLID when the most general quantum measurements are permitted. A simple analytic method as well as a physical criterion are presented for detecting whether a multipartite state is classical. To decide whether a state is generalized-classical, we provide a semi-definite programming algorithm which can be adapted for use in other unrelated contexts such as signal processing

Electrical power dissipation in carbon nanotubes on single crystal quartz and amorphous SiO2

Heat dissipation in electrically biased semiconducting carbon nanotubes (CNTs) on single crystal quartz and amorphous SiO2 is examined with temperature profiles obtained by spatially resolved Raman spectroscopy. Despite the differences in phonon velocities, thermal conductivity and van der Waals interactions with CNTs, on average, heat dissipation into single crystal quartz and amorphous SiO2 is found to be similar. Large temperature gradients and local hot spots often observed underscore the complexity of CNT temperature profiles and may be accountable for the similarities observed

Completing incomplete cohort fertility schedules

This paper develops a simple age-period-cohort framework in completing incomplete cohort fertility schedules, and makes full use of 1917--2005 U.S. data to obtain robust outcomes. Empirically, we indicate that the period effect is the key to transforming a fertility level into a fertility schedule. Accompanied by the smoothed version of tempo-variance-adjusted total fertility rates proposed in Kohler and Philipov (2001), we approximate the cohort fertility schedules fairly well and the estimates of all distributional parameters can be thereby obtained. Our approach is easy to implement and the data requirement is relatively light, indicating that the proposed method is readily applicable to countries whose data lengths are insufficiently long, and would be helpful for further empirical investigation of the relationship between cohort fertility behavior and other cohort-specific socioeconomic factors.APC model, cohort fertility schedule, fertility forecast

Cool Core Bias in Sunyaev-Zel'dovich Galaxy Cluster Surveys

Sunyaev-Zeldovich (SZ) surveys find massive clusters of galaxies by measuring the inverse Compton scattering of cosmic microwave background off of intra-cluster gas. The cluster selection function from such surveys is expected to be nearly independent of redshift and cluster astrophysics. In this work, we estimate the effect on the observed SZ signal of centrally-peaked gas density profiles (cool cores) and radio emission from the brightest cluster galaxy (BCG) by creating mock observations of a sample of clusters that span the observed range of classical cooling rates and radio luminosities. For each cluster, we make simulated SZ observations by the South Pole Telescope and characterize the cluster selection function, but note that our results are broadly applicable to other SZ surveys. We find that the inclusion of a cool core can cause a change in the measured SPT significance of a cluster between 0.01% - 10% at z > 0.3, increasing with cuspiness of the cool core and angular size on the sky of the cluster (i.e., decreasing redshift, increasing mass). We provide quantitative estimates of the bias in the SZ signal as a function of a gas density cuspiness parameter, redshift, mass, and the 1.4 GHz radio luminosity of the central AGN. Based on this work, we estimate that, for the Phoenix cluster (one of the strongest cool cores known), the presence of a cool core is biasing the SZ significance high by ~ 6%. The ubiquity of radio galaxies at the centers of cool core clusters will offset the cool core bias to varying degrees.Comment: 8 pages, 4 figures, accepted to Ap

On the standard errors of Oaxaca-type decompositions for inter-industry gender wage differentials

Horrace and Oaxaca (2001) treat the regressors in gender wage gap by industry measures as non-stochastic when computing the corresponding standard errors. However, the non-stochastic regressors assumption is thought to be inappropriate in modern econometrics. In this paper, we derive the correct standard errors for the measures proposed by Horrace and Oaxaca (2001). We then empirically apply the derived correct standard errors in regard to the March 1998 Current Population Survey data adopted in Horrace and Oaxaca (2001), as well as the Manpower Utilization Survey in the Taiwan area conducted by the Census Bureau over the years from 1978 to 2003. The empirical results suggest that the researchers would be better to use the correct standard errors derived in this paper, accompanied by the White correction, to arrive at a more accurate statistical inference.Gender wage gap

Revisiting chaos in stimulus-driven spiking networks: signal encoding and discrimination

Highly connected recurrent neural networks often produce chaotic dynamics, meaning their precise activity is sensitive to small perturbations. What are the consequences for how such networks encode streams of temporal stimuli? On the one hand, chaos is a strong source of randomness, suggesting that small changes in stimuli will be obscured by intrinsically generated variability. On the other hand, recent work shows that the type of chaos that occurs in spiking networks can have a surprisingly low-dimensional structure, suggesting that there may be "room" for fine stimulus features to be precisely resolved. Here we show that strongly chaotic networks produce patterned spikes that reliably encode time-dependent stimuli: using a decoder sensitive to spike times on timescales of 10's of ms, one can easily distinguish responses to very similar inputs. Moreover, recurrence serves to distribute signals throughout chaotic networks so that small groups of cells can encode substantial information about signals arriving elsewhere. A conclusion is that the presence of strong chaos in recurrent networks does not prohibit precise stimulus encoding.Comment: 8 figure