Deterministic Constructions of Binary Measurement Matrices from Finite Geometry

Deterministic constructions of measurement matrices in compressed sensing (CS) are considered in this paper. The constructions are inspired by the recent discovery of Dimakis, Smarandache and Vontobel which says that parity-check matrices of good low-density parity-check (LDPC) codes can be used as {provably} good measurement matrices for compressed sensing under $\ell_1$-minimization. The performance of the proposed binary measurement matrices is mainly theoretically analyzed with the help of the analyzing methods and results from (finite geometry) LDPC codes. Particularly, several lower bounds of the spark (i.e., the smallest number of columns that are linearly dependent, which totally characterizes the recovery performance of $\ell_0$-minimization) of general binary matrices and finite geometry matrices are obtained and they improve the previously known results in most cases. Simulation results show that the proposed matrices perform comparably to, sometimes even better than, the corresponding Gaussian random matrices. Moreover, the proposed matrices are sparse, binary, and most of them have cyclic or quasi-cyclic structure, which will make the hardware realization convenient and easy.Comment: 12 pages, 11 figure

Differentiable Programming Tensor Networks

Differentiable programming is a fresh programming paradigm which composes parameterized algorithmic components and trains them using automatic differentiation (AD). The concept emerges from deep learning but is not only limited to training neural networks. We present theory and practice of programming tensor network algorithms in a fully differentiable way. By formulating the tensor network algorithm as a computation graph, one can compute higher order derivatives of the program accurately and efficiently using AD. We present essential techniques to differentiate through the tensor networks contractions, including stable AD for tensor decomposition and efficient backpropagation through fixed point iterations. As a demonstration, we compute the specific heat of the Ising model directly by taking the second order derivative of the free energy obtained in the tensor renormalization group calculation. Next, we perform gradient based variational optimization of infinite projected entangled pair states for quantum antiferromagnetic Heisenberg model and obtain start-of-the-art variational energy and magnetization with moderate efforts. Differentiable programming removes laborious human efforts in deriving and implementing analytical gradients for tensor network programs, which opens the door to more innovations in tensor network algorithms and applications.Comment: Typos corrected, discussion and refs added; revised version accepted for publication in PRX. Source code available at https://github.com/wangleiphy/tensorgra

Fermionic symmetry-protected topological state in strained graphene

The low-energy physics of graphene is described by relativistic Dirac fermions with spin and valley degrees of freedom. Mechanical strain can be used to create a pseudo magnetic field pointing to opposite directions in the two valleys. We study interacting electrons in graphene exposed to both an external real magnetic field and a strain-induced pseudo magnetic field. For a certain ratio between these two fields, it is proposed that a fermionic symmetry-protected topological state can be realized. The state is characterized in detail using model wave functions, Chern-Simons field theory, and numerical calculations. Our paper suggests that graphene with artificial gauge fields may host a rich set of topological states.Comment: 8 pages, 4 figure

Research on Tomography by Using Seismic Reflection Wave in Laneway

AbstractAs a necessary step and an integral part of works in laneway, the geological prediction is an important means of reducing disaster losses and geological disasters in the works in laneway. This paper mainly discusses tunnel reflection tomography by using seismic reflection wave in laneway. The speed of elastic wave in front of tunnel face and the three-dimensional images can be figured out when the detector check the reflection of elastic wave from the focal points on the tunnel face. The location, size and depth of cave can be ascertained. According to the forecasts of laneway works in the Iron Mine Xishimen, tunnel reflection tomography by using seismic reflection wave can well forecast engineering geology and hydro-geological conditions in front of tunnel face. It will help to supply positive guidance for working plan and construction measures in laneway. Therefore, the construction safety and speed can be ensured, helping to lead to great practical significance and significant economic benefits

Like-sign Di-lepton Signals in Higgsless Models at the LHC

We study the potential LHC discovery of the Z1 KK gauge boson unitarizing longitudinal W+W- scattering amplitude. In particular, we explore the decay mode Z1->t tbar along with Z1-> W+W- without specifying the branching fractions. We propose to exploit the associated production pp-> W Z1, and select the final state of like-sign dileptons plus multijets and large missing energy. We conclude that it is possible to observe the Z1 resonance at a 5 sigma level with an integrated luminosity of 100 inverse fb at the LHC upto 650 GeV for a dominant WW channel, and 560 GeV for a dominant ttbar channel.Comment: 13 pages, 7 figure