Does Standard Cosmology Express Cosmological Principle Faithfully?

In 1+1 dimensional case, Einstein equation cannot give us any information on the evolution of the universe because the Einstein tensor of the system is identically zero. We study such a 1+1 dimensional cosmology and find the metric of it according to cosmological principle and special relativity, but the results contradict the usual expression of cosmological principle of standard cosmology. So we doubt in 1+3 dimensional case, cosmological principle is expressed faithfully by standard cosmology.Comment: physical interpretation changes, but mathematica formula keep

Dependence in Propositional Logic: Formula-Formula Dependence and Formula Forgetting -- Application to Belief Update and Conservative Extension

Dependence is an important concept for many tasks in artificial intelligence. A task can be executed more efficiently by discarding something independent from the task. In this paper, we propose two novel notions of dependence in propositional logic: formula-formula dependence and formula forgetting. The first is a relation between formulas capturing whether a formula depends on another one, while the second is an operation that returns the strongest consequence independent of a formula. We also apply these two notions in two well-known issues: belief update and conservative extension. Firstly, we define a new update operator based on formula-formula dependence. Furthermore, we reduce conservative extension to formula forgetting.Comment: We find a mistake in this version and we need a period of time to fix i

Performance Limits of Segmented Compressive Sampling: Correlated Samples versus Bits

This paper gives performance limits of the segmented compressive sampling (CS) which collects correlated samples. It is shown that the effect of correlation among samples for the segmented CS can be characterized by a penalty term in the corresponding bounds on the sampling rate. Moreover, this penalty term is vanishing as the signal dimension increases. It means that the performance degradation due to the fixed correlation among samples obtained by the segmented CS (as compared to the standard CS with equivalent size sampling matrix) is negligible for a high-dimensional signal. In combination with the fact that the signal reconstruction quality improves with additional samples obtained by the segmented CS (as compared to the standard CS with sampling matrix of the size given by the number of original uncorrelated samples), the fact that the additional correlated samples also provide new information about a signal is a strong argument for the segmented CS.Comment: 27 pages, 8 figures, Submitted to IEEE Trans. Signal Processing on November 201

Asymptotic Properties of Primal-Dual Algorithm for Distributed Stochastic Optimization Over Random Networks

This paper studies a distributed stochastic optimization problem over random networks with imperfect communications subject to a global constraint, which is the intersection of local constraint sets assigned to agents. The global cost function is the sum of local cost functions, each of which is the expectation of a random cost function. By incorporating the augmented Lagrange technique with the projection method, a stochastic approximation based distributed primal-dual algorithm is proposed to solve the problem. Each agent updates its estimate by using the local observations and the information derived from neighbors. For the constrained problem, the estimates are first shown to be bounded almost surely (a.s.), and then are proved to converge to the optimal solution set a.s. Furthermore, the asymptotic normality and efficiency of the algorithm are addressed for the unconstrained case. The results demonstrate the influence of random networks, communication noises, and gradient errors on the performance of the algorithm. Finally, numerical simulations demonstrate the theoretic results

Short-term Market Reaction after Trading Halts in Chinese Stock Market

In this paper, we study the dynamics of absolute return, trading volume and bid-ask spread after the trading halts using high-frequency data from the Shanghai Stock Exchange. We deal with all three types of trading halts, namely intraday halts, one-day halts and inter-day halts, of 203 stocks in Shanghai Stock Exchange from August 2009 to August 2011. We find that absolute return, trading volume, and in case of bid-ask spread around intraday halts share the same pattern with a sharp peak and a power law relaxation after that. While for different types of trading halts, the peaks' height and the relaxation exponents are different. From the perspective of halt reasons or halt duration, the relaxation exponents of absolute return after inter-day halts are larger than that after intraday halts and one-day halts, which implies that inter-day halts are most effective. From the perspective of price trends, the relaxation exponents of excess absolute return and excess volume for positive events are larger than that for negative events in case of intraday halts and one-day halts, implying that positive events are more effective than negative events for intraday halts and one-day halts. In contrast, negative events are more effective than positive events for inter-day halts.Comment: 11 pages, 8 figures, Physica A (2014

Low-Rank Deep Convolutional Neural Network for Multi-Task Learning

In this paper, we propose a novel multi-task learning method based on the deep convolutional network. The proposed deep network has four convolutional layers, three max-pooling layers, and two parallel fully connected layers. To adjust the deep network to multi-task learning problem, we propose to learn a low-rank deep network so that the relation among different tasks can be explored. We proposed to minimize the number of independent parameter rows of one fully connected layer to explore the relations among different tasks, which is measured by the nuclear norm of the parameter of one fully connected layer, and seek a low-rank parameter matrix. Meanwhile, we also propose to regularize another fully connected layer by sparsity penalty, so that the useful features learned by the lower layers can be selected. The learning problem is solved by an iterative algorithm based on gradient descent and back-propagation algorithms. The proposed algorithm is evaluated over benchmark data sets of multiple face attribute prediction, multi-task natural language processing, and joint economics index predictions. The evaluation results show the advantage of the low-rank deep CNN model over multi-task problems

Thermodynamics of the α\alpha-γ\gamma transition in cerium studied by an LDA + Gutzwiller method

The $\alpha$-$\gamma$ transition in cerium has been studied in both zero and finite temperature by Gutzwiller density functional theory. We find that the first order transition between $\alpha$ and $\gamma$ phases persists to the zero temperature with negative pressure. By further including the entropy contributed by both electronic quasi-particles and lattice vibration, we obtain the total free energy at given volume and temperature, from which we obtain the $\alpha$-$\gamma$ transition from the first principle calculation. We also computed the phase diagram and pressure versus volume isotherms of cerium at finite temperature and pressure, finding excellent agreement with the experiments. Our calculation indicate that both the electronic entropy and lattice vibration entropy plays important role in the $\alpha$-$\gamma$ transition.Comment: 5 pages, 4 figure

Permutation Meets Parallel Compressed Sensing: How to Relax Restricted Isometry Property for 2D Sparse Signals

Traditional compressed sensing considers sampling a 1D signal. For a multidimensional signal, if reshaped into a vector, the required size of the sensing matrix becomes dramatically large, which increases the storage and computational complexity significantly. To solve this problem, we propose to reshape the multidimensional signal into a 2D signal and sample the 2D signal using compressed sensing column by column with the same sensing matrix. It is referred to as parallel compressed sensing, and it has much lower storage and computational complexity. For a given reconstruction performance of parallel compressed sensing, if a so-called acceptable permutation is applied to the 2D signal, we show that the corresponding sensing matrix has a smaller required order of restricted isometry property condition, and thus, storage and computation requirements are further lowered. A zigzag-scan-based permutation, which is shown to be particularly useful for signals satisfying a layer model, is introduced and investigated. As an application of the parallel compressed sensing with the zigzag-scan-based permutation, a video compression scheme is presented. It is shown that the zigzag-scan-based permutation increases the peak signal-to-noise ratio of reconstructed images and video frames.Comment: 30 pages, 10 figures, 3 tables, submitted to the IEEE Trans. Signal Processing in November 201

Lateral Migration and Nonuniform Rotation of Biconcave Particle Suspended in Poiseuille Flow

A biconcave particle suspended in a Poiseuille flow is investigated by the multiple-relaxation-time lattice Boltzmann method with the Galilean-invariant momentum exchange method. The lateral migration and equilibrium of the particle are similar to the Segr\'e-Silberberg effect in our numerical simulations. Surprisingly, two lateral equilibrium positions are observed corresponding to the releasing positions of the biconcave particle. The upper equilibrium positions significantly decrease with the growth of the Reynolds number, whereas the lower ones are almost insensitive to the Reynolds number. Interestingly, the regular wave accompanied by nonuniform rotation is exhibited in the lateral movement of the biconcave particle. It can be attributed to that the biconcave shape in various postures interacts with the parabolic velocity distribution of the Poiseuille flow. A set of contours illustrate the dynamic flow field when the biconcave particle has successive postures in a rotating period.Comment: 13 pages, 5 figure

Vortex lattice and vortex bound states in CsFe2_2As2_2 investigated by scanning tunneling microscopy/spectroscopy

We investigate the vortex lattice and vortex bound states in CsFe$_2$As$_2$ single crystals by scanning tunneling microscopy/spectroscopy (STM/STS) under various magnetic fields. A possible structural transition or crossover of vortex lattice is observed with the increase of magnetic field, i.e., the vortex lattice changes from a distorted hexagonal lattice to a distorted tetragonal one at the magnetic field near 0.5 T. It is found that a mixture of stripelike hexagonal and square vortex lattices emerges in the crossover region. The vortex bound state is also observed in the vortex center. The tunneling spectra crossing a vortex show that the bound-state peak position holds near zero bias with STM tip moving away from the vortex core center. The Fermi energy estimated from the vortex bound state energy is very small. Our investigations provide experimental information to both the vortex lattice and the vortex bound states in this iron-based superconductor.Comment: 7 pages, 5 figure