The upper critical field and its anisotropy in LiFeAs

The upper critical field $\mu_0H_{c2}(T_c)$ of LiFeAs single crystals has been determined by measuring the electrical resistivity using the facilities of pulsed magnetic field at Los Alamos. We found that $\mu_0H_{c2}(T_c)$ of LiFeAs shows a moderate anisotropy among the layered iron-based superconductors; its anisotropic parameter $\gamma$ monotonically decreases with decreasing temperature and approaches $\gamma\simeq 1.5$ as $T\rightarrow 0$. The upper critical field reaches 15T ($H\parallel c$) and 24.2T ($H\parallel ab$) at $T=$1.4K, which value is much smaller than other iron-based high $T_c$ superconductors. The temperature dependence of $\mu_0H_{c2}(T_c)$ can be described by the Werthamer-Helfand-Hohenberg (WHH) method, showing orbitally and (likely) spin-paramagnetically limited upper critical field for $H\parallel c$ and $H\parallel ab$, respectively.Comment: 5 pages,5 figure

Genome-wide profiling of uncapped mRNA

Gene transcripts are under extensive posttranscriptional regulation, including the regulation of their stability. A major route for mRNA degradation produces uncapped mRNAs, which can be generated by decapping enzymes, endonucleases, and small RNAs. Profiling uncapped mRNA molecules is important for the understanding of the transcriptome, whose composition is determined by a balance between mRNA synthesis and degradation. In this chapter, we describe a method to profile these uncapped mRNAs at the genome scale

Reduced dynamics with renormalization in solid-state charge qubit measurement

Quantum measurement will inevitably cause backaction on the measured system, resulting in the well known dephasing and relaxation. In this report, in the context of solid--state qubit measurement by a mesoscopic detector, we show that an alternative backaction known as renormalization is important under some circumstances. This effect is largely overlooked in the theory of quantum measurement.Comment: 12 pages, 4 figure

Synergy of Ag and AgBr in a Pressurized Flow Reactor for Selective Photocatalytic Oxidative Coupling of Methane

Oxidation of methane into valuable chemicals, such as C2+ molecules, has been long sought after but the dilemma between high yield and high selectivity of desired products remains. Herein, methane is upgraded through the photocatalytic oxidative coupling of methane (OCM) over a ternary Ag-AgBr/TiO2 catalyst in a pressurized flow reactor. The ethane yield of 35.4 μmol/h with a high C2+ selectivity of 79% has been obtained under 6 bar pressure. These are much better than most of the previous benchmark performance in photocatalytic OCM processes. These results are attributed to the synergy between Ag and AgBr, where Ag serves as an electron acceptor and promotes the charge transfer and AgBr forms a heterostructure with TiO2 not only to facilitate charge separation but also to avoid the overoxidation process. This work thus demonstrates an efficient strategy for photocatalytic methane conversion by both the rational design of the catalyst for the high selectivity and reactor engineering for the high conversion

A unified primal dual active set algorithm for nonconvex sparse recovery

In this paper, we consider the problem of recovering a sparse signal based on penalized least squares formulations. We develop a novel algorithm of primal-dual active set type for a class of nonconvex sparsity-promoting penalties, including ℓ 0 , bridge, smoothly clipped absolute deviation, capped ℓ 1 and minimax concavity penalty. First, we establish the existence of a global minimizer for the related optimization problems. Then we derive a novel necessary optimality condition for the global minimizer using the associated thresholding operator. The solutions to the optimality system are coordinatewise minimizers, and under minor conditions, they are also local minimizers. Upon introducing the dual variable, the active set can be determined using the primal and dual variables together. Further, this relation lends itself to an iterative algorithm of active set type which at each step involves first updating the primal variable only on the active set and then updating the dual variable explicitly. When combined with a continuation strategy on the regularization parameter, the primal dual active set method is shown to converge globally to the underlying regression target under certain regularity conditions. Extensive numerical experiments with both simulated and real data demonstrate its superior performance in terms of computational efficiency and recovery accuracy compared with the existing sparse recovery methods

Approximate perturbed direct homotopy reduction method: infinite series reductions to two perturbed mKdV equations

An approximate perturbed direct homotopy reduction method is proposed and applied to two perturbed modified Korteweg-de Vries (mKdV) equations with fourth order dispersion and second order dissipation. The similarity reduction equations are derived to arbitrary orders. The method is valid not only for single soliton solution but also for the Painlev\'e II waves and periodic waves expressed by Jacobi elliptic functions for both fourth order dispersion and second order dissipation. The method is valid also for strong perturbations.Comment: 8 pages, 1 figur

Optimization of generator coordinate method with machine-learning algorithms for nuclear spectra and neutrinoless double-beta decay

The generator coordinate method (GCM) is an important tool of choice for modeling large-amplitude collective motion in atomic nuclei. Recently, it has attracted increasing interest as it can be exploited to extend ab initio methods to the description of collective excitations of medium-mass and heavy deformed nuclei, as well as the nuclear matrix elements (NME) of candidates for neutrinoless double-beta (NLDBD) decay. The computational complexity of the GCM increases rapidly with the number of collective coordinates. It imposes a strong restriction on the applicability of the method. We aim to exploit machine learning (ML) algorithms to speed up GCM calculations and ultimately provide a more efficient description of nuclear energy spectra and other observables such as the NME of NLDBD decay without loss of accuracy. To speed up GCM calculations, we propose a subspace reduction algorithm that employs optimized ML models as surrogates for exact quantum-number projection calculations for norm and Hamiltonian kernels. The model space of the original GCM is reduced to a subspace relevant for nuclear low energy spectra and the NME of ground state to ground state $0\nu\beta\beta$ decay based on the orthogonality condition (OC) and the energy transition-orthogonality procedure (ENTROP), respectively. For simplicity, a polynomial regression algorithm is used to learn the norm and Hamiltonian kernels. The efficiency and accuracy of this algorithm are illustrated for 76Ge and 76Se by comparing results obtained using the ML models to direct GCM calculations. The results show that the performance of the GCM+OC/ENTROP+ML is more robust than that of the GCM+ML alone, and the former can reproduce the results of the original GCM calculation rather accurately with a significantly reduced computational cost.Comment: 14 pages with 18 figure