Alternating Back-Propagation for Generator Network

This paper proposes an alternating back-propagation algorithm for learning the generator network model. The model is a non-linear generalization of factor analysis. In this model, the mapping from the continuous latent factors to the observed signal is parametrized by a convolutional neural network. The alternating back-propagation algorithm iterates the following two steps: (1) Inferential back-propagation, which infers the latent factors by Langevin dynamics or gradient descent. (2) Learning back-propagation, which updates the parameters given the inferred latent factors by gradient descent. The gradient computations in both steps are powered by back-propagation, and they share most of their code in common. We show that the alternating back-propagation algorithm can learn realistic generator models of natural images, video sequences, and sounds. Moreover, it can also be used to learn from incomplete or indirect training data

Relativistic mean-field approximation with density-dependent screening meson masses in nuclear matter

The Debye screening masses of the $\sigma$, $\omega$ and neutral $\rho$ mesons and the photon are calculated in the relativistic mean-field approximation. As the density of the nucleon increases, all the screening masses of mesons increase. It shows a different result with Brown-Rho scaling, which implies a reduction in the mass of all the mesons in the nuclear matter except the pion. Replacing the masses of the mesons with their corresponding screening masses in Walecka-1 model, five saturation properties of the nuclear matter are fixed reasonably, and then a density-dependent relativistic mean-field model is proposed without introducing the non-linear self-coupling terms of mesons.Comment: 14 pages, 3 figures, REVTEX4, Accepted for publication in Int. J. Mod. Phys.

Learning Generative ConvNets via Multi-grid Modeling and Sampling

This paper proposes a multi-grid method for learning energy-based generative ConvNet models of images. For each grid, we learn an energy-based probabilistic model where the energy function is defined by a bottom-up convolutional neural network (ConvNet or CNN). Learning such a model requires generating synthesized examples from the model. Within each iteration of our learning algorithm, for each observed training image, we generate synthesized images at multiple grids by initializing the finite-step MCMC sampling from a minimal 1 x 1 version of the training image. The synthesized image at each subsequent grid is obtained by a finite-step MCMC initialized from the synthesized image generated at the previous coarser grid. After obtaining the synthesized examples, the parameters of the models at multiple grids are updated separately and simultaneously based on the differences between synthesized and observed examples. We show that this multi-grid method can learn realistic energy-based generative ConvNet models, and it outperforms the original contrastive divergence (CD) and persistent CD.Comment: CVPR 201

In-Process Global Interpretation for Graph Learning via Distribution Matching

Graphs neural networks (GNNs) have emerged as a powerful graph learning model due to their superior capacity in capturing critical graph patterns. To gain insights about the model mechanism for interpretable graph learning, previous efforts focus on post-hoc local interpretation by extracting the data pattern that a pre-trained GNN model uses to make an individual prediction. However, recent works show that post-hoc methods are highly sensitive to model initialization and local interpretation can only explain the model prediction specific to a particular instance. In this work, we address these limitations by answering an important question that is not yet studied: how to provide global interpretation of the model training procedure? We formulate this problem as in-process global interpretation, which targets on distilling high-level and human-intelligible patterns that dominate the training procedure of GNNs. We further propose Graph Distribution Matching (GDM) to synthesize interpretive graphs by matching the distribution of the original and interpretive graphs in the feature space of the GNN as its training proceeds. These few interpretive graphs demonstrate the most informative patterns the model captures during training. Extensive experiments on graph classification datasets demonstrate multiple advantages of the proposed method, including high explanation accuracy, time efficiency and the ability to reveal class-relevant structure.Comment: Under Revie

Poly[diimidazole-μ4-oxalato-μ2-oxalato-dicopper(II)]

The title compound, [Cu2(C2O4)2(C3H4N2)2]n, was obtained as an unexpected product under hydro­thermal conditions. The CuII atom is in a Jahn–Teller-distorted octa­hedral environment formed by one imidazole N atom and five O atoms from three oxalate anions. The two independent oxalate anions are situated on centres of inversion and coordinate to the CuII atom in two different modes, viz. bidentate and monodentate. The bidentate anions bridge two CuII atoms, whereas the monodentate anions bridge four CuII atoms, leading to a layered arrangement parallel to (100). These layers are further linked into a final three-dimensional network structure via inter­molecular N—H⋯O hydrogen bonds. The title compound is isotypic with the Zn analogue

Effective photon mass in nuclear matter and finite nuclei

Electromagnetic field in nuclear matter and nuclei are studied. In the nuclear matter, because the expectation value of the electric charge density operator is not zero, different in vacuum, the U(1) local gauge symmetry of electric charge is spontaneously broken, and consequently, the photon gains an effective mass through the Higgs mechanism. An alternative way to study the effective mass of photon is to calculate the self-energy of photon perturbatively. It shows that the effective mass of photon is about $5.42MeV$ in the symmetric nuclear matter at the saturation density $\rho_0 = 0.16fm^{-3}$ and about $2.0MeV$ at the surface of ${}^{238}U$. It seems that the two-body decay of a massive photon causes the sharp lines of electron-positron pairs in the low energy heavy ion collision experiments of ${}^{238}U+{}^{232}Th$ .Comment: 10 pages, 2 figures, 1 table, REVTEX4, submitted to Int. J. Mod. Phys.