Cosmological constraints on Λ(α)\Lambda(\alpha)CDM models with time-varying fine structure constant

We study the $\Lambda(\alpha)$CDM models with $\Lambda(\alpha)$ being a function of the time-varying fine structure constant $\alpha$. We give a close look at the constraints on two specific $\Lambda(\alpha)$CDM models with one and two model parameters, respectively, based on the cosmological observational measurements along with 313 data points for the time-varying $\alpha$. We find that the model parameters are constrained to be around $10^{-4}$, which are similar to the results discussed previously but more accurately.Comment: 16 pages, 4 figures, published in Annals of Physics 397 (2018) 400-40

Modified holographic Ricci dark energy coupled to interacting relativistic and non-relativistic dark matter in the nonflat universe

The modified holographic Ricci dark energy coupled to interacting relativistic and non-relativistic dark matter is considered in the nonflat Friedmann-Robertson-Walker universe. Through examining the deceleration parameter, one can find that the transition time of the Universe from decelerating to accelerating phase in the interacting holographic Ricci dark energy model is close to that in the $\Lambda$ cold dark matter model. The evolution of modified holographic Ricci dark energy's state parameter and the evolution of dark matter and dark energy's densities shows that the dark energy holds the dominant position from the near past to the future. By studying the statefinder diagnostic and the evolution of the total pressure, one can find that this model could explain the Universe's transition from the radiation to accelerating expansion stage through the dust stage. According to the $Om$ diagnostic, it is easy to find that when the interaction is weak and the proportion of relativistic dark matter in total dark matter is small, this model is phantom-like. Through our studying, we find the interaction and the relativistic dark matter's proportion all have great influence on the evolution of the Universe.Comment: 14 pages, 8 figure

Steep Decay of GRB X-ray Flares: the Result of Anisotropic Synchrotron Radiation

When an emitting spherical shell with a constant Lorentz factor turns off emission abruptly at some radius, its high-latitude emission would obey the relation of $\hat{\alpha}$ (the temporal index) = $2 + \hat{\beta}$ (the spectral index). However, this relation is violated by the X-ray fares in some gamma-ray bursts (GRBs), whose $\hat{\alpha}$ is much more steeper. We show that the synchrotron radiation should be anisotropic when the angular distribution of accelerated electrons has preferable orientation, and this anisotropy would naturally lead to the steeper decay for the high-latitude emission if the the intrinsic emission is limb-brightened. We use our simple toy model to reproduce the temporal and the spectral evolution of the X-ray flares. We show that our model can well interpret the steep decay of the X-ray flares in the three GRBs selected as example. Recent simulations on particle acceleration may support the specific anisotropic distribution of the electrons adopted in our work. Reversely, confirmation of the anisotropy in the radiation would give meaningful clues to the details of electron acceleration in the emitting region.Comment: 12 pages, 1 figure, ApJL accepte

Observational constraints on running vacuum model

We investigate the power spectra of the CMB temperature and matter density in the running vacuum model (RVM) with the time-dependent cosmological constant of $\Lambda = 3 \nu H^2 + \Lambda_0$, where $H$ is the Hubble parameter. In this model, dark energy decreases in time and decays to both matter and radiation. By using the Markov chain Monte Carlo method, we constrain the model parameter $\nu$ as well as the cosmological observables. Explicitly, we obtain $\nu \leq 1.54 \times 10^{-4}$ (68\% confidence level) in the RVM with the best-fit $\chi^2_{\mathrm{RVM}} = 13968.8$, which is slightly smaller than $\chi^2_{\Lambda \mathrm{CDM}} = 13969.8$ in the $\Lambda$CDM model of $\nu=0$.Comment: 13 pages, 3 figures, to be published in Chinese Physics

Linear NDCG and Pair-wise Loss

Linear NDCG is used for measuring the performance of the Web content quality assessment in ECML/PKDD Discovery Challenge 2010. In this paper, we will prove that the DCG error equals a new pair-wise loss.Comment: 5 pages, 3 figure

Learning to Sketch Human Facial Portraits using Personal Styles by Case-Based Reasoning

This paper employs case-based reasoning (CBR) to capture the personal styles of individual artists and generate the human facial portraits from photos accordingly. For each human artist to be mimicked, a series of cases are firstly built-up from her/his exemplars of source facial photo and hand-drawn sketch, and then its stylization for facial photo is transformed as a style-transferring process of iterative refinement by looking-for and applying best-fit cases in a sense of style optimization. Two models, fitness evaluation model and parameter estimation model, are learned for case retrieval and adaptation respectively from these cases. The fitness evaluation model is to decide which case is best-fitted to the sketching of current interest, and the parameter estimation model is to automate case adaptation. The resultant sketch is synthesized progressively with an iterative loop of retrieval and adaptation of candidate cases until the desired aesthetic style is achieved. To explore the effectiveness and advantages of the novel approach, we experimentally compare the sketch portraits generated by the proposed method with that of a state-of-the-art example-based facial sketch generation algorithm as well as a couple commercial software packages. The comparisons reveal that our CBR based synthesis method for facial portraits is superior both in capturing and reproducing artists' personal illustration styles to the peer methods

FPDeep: Scalable Acceleration of CNN Training on Deeply-Pipelined FPGA Clusters

Deep Neural Networks (DNNs) have revolutionized numerous applications, but the demand for ever more performance remains unabated. Scaling DNN computations to larger clusters is generally done by distributing tasks in batch mode using methods such as distributed synchronous SGD. Among the issues with this approach is that to make the distributed cluster work with high utilization, the workload distributed to each node must be large, which implies nontrivial growth in the SGD mini-batch size. In this paper, we propose a framework called FPDeep, which uses a hybrid of model and layer parallelism to configure distributed reconfigurable clusters to train DNNs. This approach has numerous benefits. First, the design does not suffer from batch size growth. Second, novel workload and weight partitioning leads to balanced loads of both among nodes. And third, the entire system is a fine-grained pipeline. This leads to high parallelism and utilization and also minimizes the time features need to be cached while waiting for back-propagation. As a result, storage demand is reduced to the point where only on-chip memory is used for the convolution layers. We evaluate FPDeep with the Alexnet, VGG-16, and VGG-19 benchmarks. Experimental results show that FPDeep has good scalability to a large number of FPGAs, with the limiting factor being the FPGA-to-FPGA bandwidth. With 6 transceivers per FPGA, FPDeep shows linearity up to 83 FPGAs. Energy efficiency is evaluated with respect to GOPs/J. FPDeep provides, on average, 6.36x higher energy efficiency than comparable GPU servers.Comment: Accepted by IEEE TRANSACTIONS ON COMPUTERS (TC

Stochastic Conjugate Gradient Algorithm with Variance Reduction

Conjugate gradient (CG) methods are a class of important methods for solving linear equations and nonlinear optimization problems. In this paper, we propose a new stochastic CG algorithm with variance reduction and we prove its linear convergence with the Fletcher and Reeves method for strongly convex and smooth functions. We experimentally demonstrate that the CG with variance reduction algorithm converges faster than its counterparts for four learning models, which may be convex, nonconvex or nonsmooth. In addition, its area under the curve performance on six large-scale data sets is comparable to that of the LIBLINEAR solver for the L2-regularized L2-loss but with a significant improvement in computational efficiencyComment: 10 pages, 4 figures, appeared in IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, CGVR algorithm is available on github: https://github.com/xbjin/cgv

A Pixel-Based Framework for Data-Driven Clothing

With the aim of creating virtual cloth deformations more similar to real world clothing, we propose a new computational framework that recasts three dimensional cloth deformation as an RGB image in a two dimensional pattern space. Then a three dimensional animation of cloth is equivalent to a sequence of two dimensional RGB images, which in turn are driven/choreographed via animation parameters such as joint angles. This allows us to leverage popular CNNs to learn cloth deformations in image space. The two dimensional cloth pixels are extended into the real world via standard body skinning techniques, after which the RGB values are interpreted as texture offsets and displacement maps. Notably, we illustrate that our approach does not require accurate unclothed body shapes or robust skinning techniques. Additionally, we discuss how standard image based techniques such as image partitioning for higher resolution, GANs for merging partitioned image regions back together, etc., can readily be incorporated into our framework

A bound system in the expanding universe with modified holographic Ricci dark energy and dark matter

The evolution of a bound system in the expanding background has been investigated in this paper. The background is described by a FRW universe with the modified holographic dark energy model, whose equation of state parameter changes with time and can cross the phantom boundary. To study the evolution of the bound system, an interpolating metric is considered, and on this basis the geodesics of a test particle are given. The equation of motion and the effective potential are also derived from the geodesics. By studying the the effective potential and the evolution of the radius of a test particle in the bound system of the Milky Way galaxy, we have found that the galaxy would go through three stages: expands from a singular point; stays in a discoid for a period of time; big rip in the future. With the help of analysing the critical angular momentum, we find that the test particle needs less angular momentum to escape from the center mass as time passes.Comment: 9 pages, 7 figure