Comment on "Single-mode excited entangled coherent states"

In Xu and Kuang (\textit{J. Phys. A: Math. Gen.} 39 (2006) L191), the authors claim that, for single-mode excited entangled coherent states $| \Psi_{\pm}(\alpha,m)>$, \textquotedblleft the photon excitations lead to the decrease of the concurrence in the strong field regime of $| \alpha | ^{2}$ and the concurrence tends to zero when $| \alpha | ^{2}\to \infty$". This is wrong.Comment: 4 apges, 2 figures, submitted to JPA 15 April 200

Phenological response of vegetation to upstream river flow in the Heihe Rive basin by time series analysis of MODIS data

Liquid and solid precipitation is abundant in the high elevation, upper reach of the Heihe River basin in northwestern China. The development of modern irrigation schemes in the middle reach of the basin is taking up an increasing share of fresh water resources, endangering the oasis and traditional irrigation systems in the lower reach. In this study, the response of vegetation in the Ejina Oasis in the lower reach of the Heihe River to the water yield of the upper catchment was analyzed by time series analysis of monthly observations of precipitation in the upper and lower catchment, river streamflow downstream of the modern irrigation schemes and satellite observations of vegetation index. Firstly, remotely sensed NDVI data acquired by Terra-MODIS are used to monitor the vegetation dynamic for a seven years period between 2000 and 2006. Due to cloud-contamination, atmospheric influence and different solar and viewing angles, however, the quality and consistence of time series of remotely sensed NDVI data are degraded. A Fourier Transform method – the Harmonic Analysis of Time Series (HANTS) algorithm – is used to reconstruct cloud- and noise-free NDVI time series data from the Terra-MODIS NDVI dataset. Modification is made on HANTS by adding additional parameters to deal with large data gaps in yearly time series in combination with a Temporal-Similarity-Statistics (TSS) method developed in this study to seek for initial values for the large gap periods. Secondly, the same Fourier Transform method is used to model time series of the vegetation phenology. The reconstructed cloud-free NDVI time series data are used to study the relationship between the water availability (i.e. the local precipitation and upstream water yield) and the evolution of vegetation conditions in Ejina Oasis from 2000 to 2006. Anomalies in precipitation, streamflow, and vegetation index are detected by comparing each year with the average year. The results showed that: the previous year total runoff had a significant relationship with the vegetation growth in Ejina Oasis and that anomalies in the spring monthly runoff of the Heihe River influenced the phenology of vegetation in the entire oasis. Warmer climate expressed by the degree-days showed positive influence on the vegetation phenology in particular during drier years. The time of maximum green-up is uniform throughout the oasis during wetter years, but showed a clear S-N gradient (downstream) during drier years

Fluctuations of the vacuum energy density of quantum fields in curved spacetime via generalized zeta functions

For quantum fields on a curved spacetime with an Euclidean section, we derive a general expression for the stress energy tensor two-point function in terms of the effective action. The renormalized two-point function is given in terms of the second variation of the Mellin transform of the trace of the heat kernel for the quantum fields. For systems for which a spectral decomposition of the wave opearator is possible, we give an exact expression for this two-point function. Explicit examples of the variance to the mean ratio $\Delta' = (-^2)/(^2)$ of the vacuum energy density $\rho$ of a massless scalar field are computed for the spatial topologies of $R^d\times S^1$ and $S^3$, with results of $\Delta'(R^d\times S^1) =(d+1)(d+2)/2$, and $\Delta'(S^3) = 111$ respectively. The large variance signifies the importance of quantum fluctuations and has important implications for the validity of semiclassical gravity theories at sub-Planckian scales. The method presented here can facilitate the calculation of stress-energy fluctuations for quantum fields useful for the analysis of fluctuation effects and critical phenomena in problems ranging from atom optics and mesoscopic physics to early universe and black hole physics.Comment: Uses revte

Multiple phase transitions in single-crystalline Na1−δ_{1-\delta}FeAs

Specific heat, resistivity, susceptibility and Hall coefficient measurements were performed on high-quality single crystalline Na$_{1-\delta}$FeAs. This compound is found to undergo three successive phase transitions at around 52, 41, and 23 K, which correspond to structural, magnetic and superconducting transitions, respectively. The Hall effect result indicates the development of energy gap at low temperature due to the occurrence of spin-density-wave instability. Our results provide direct experimental evidence of the magnetic ordering in the nearly stoichiometric NaFeAs.Comment: 4 pages, 4 figure

Gravity and Nonequilibrium Thermodynamics of Classical Matter

Renewed interest in deriving gravity (more precisely, the Einstein equations) from thermodynamics considerations [1, 2] is stirred up by a recent proposal that 'gravity is an entropic force' [3] (see also [4]). Even though I find the arguments justifying such a claim in this latest proposal rather ad hoc and simplistic compared to the original one I would unreservedly support the call to explore deeper the relation between gravity and thermodynamics, this having the same spirit as my long-held view that general relativity is the hydrodynamic limit [5, 6] of some underlying theories for the microscopic structure of spacetime - all these proposals, together with that of [7, 8], attest to the emergent nature of gravity [9]. In this first paper of two we set the modest goal of studying the nonequilibrium thermodynamics of classical matter only, bringing afore some interesting prior results, without invoking any quantum considerations such as Bekenstein-Hawking entropy, holography or Unruh effect. This is for the sake of understanding the nonequilibrium nature of classical gravity which is at the root of many salient features of black hole physics. One important property of gravitational systems, from self-gravitating gas to black holes, is their negative heat capacity, which is the source of many out-of-the ordinary dynamical and thermodynamic features such as the non-existence in isolated systems of thermodynamically stable configurations, which actually provides the condition for gravitational stability. A related property is that, being systems with long range interaction, they are nonextensive and relax extremely slowly towards equilibrium. Here we explore how much of the known features of black hole thermodynamics can be derived from this classical nonequilibrium perspective. A sequel paper will address gravity and nonequilibrium thermodynamics of quantum fields [10].Comment: 25 pages essay. Invited Talk at Mariofest, March 2010, Rosario, Argentina. Festschrift to appear as an issue of IJMP

Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks

While the use of bottom-up local operators in convolutional neural networks (CNNs) matches well some of the statistics of natural images, it may also prevent such models from capturing contextual long-range feature interactions. In this work, we propose a simple, lightweight approach for better context exploitation in CNNs. We do so by introducing a pair of operators: gather, which efficiently aggregates feature responses from a large spatial extent, and excite, which redistributes the pooled information to local features. The operators are cheap, both in terms of number of added parameters and computational complexity, and can be integrated directly in existing architectures to improve their performance. Experiments on several datasets show that gather-excite can bring benefits comparable to increasing the depth of a CNN at a fraction of the cost. For example, we find ResNet-50 with gather-excite operators is able to outperform its 101-layer counterpart on ImageNet with no additional learnable parameters. We also propose a parametric gather-excite operator pair which yields further performance gains, relate it to the recently-introduced Squeeze-and-Excitation Networks, and analyse the effects of these changes to the CNN feature activation statistics.Comment: NeurIPS 201