Background field method in the large NfN_f expansion of scalar QED

Using the background field method, we, in the large $N_f$ approximation, calculate the beta function of scalar quantum electrodynamics at the first nontrivial order in $1/N_f$ by two different ways. In the first way, we get the result by summing all the graphs contributing directly. In the second way, we begin with the Borel transform of the related two point Green's function. The main results are that the beta function is fully determined by a simple function and can be expressed as an analytic expression with a finite radius of convergence, and the scheme-dependent renormalized Borel transform of the two point Green's function suffers from renormalons.Comment: 13 pages, 4 figures, 1 table, to appear in the European Physical Journal

Self-assembly of Nanometer-scale Magnetic Dots with Narrow Size Distributions on an Insulating Substrate

The self-assembly of iron dots on the insulating surface of NaCl(001) is investigated experimentally and theoretically. Under proper growth conditions, nanometer-scale magnetic iron dots with remarkably narrow size distributions can be achieved in the absence of a wetting layer Furthermore, both the vertical and lateral sizes of the dots can be tuned with the iron dosage without introducing apparent size broadening, even though the clustering is clearly in the strong coarsening regime. These observations are interpreted using a phenomenological mean-field theory, in which a coverage-dependent optimal dot size is selected by strain-mediated dot-dot interactions.Comment: 5 pages, 4 figure

Shotgun proteomic analysis of mulberry dwarf phytoplasma

<p>Abstract</p> <p>Background</p> <p>Mulberry dwarf (MD), which is caused by phytoplasma, is one of the most serious infectious diseases of mulberry. Phytoplasmas have been associated with diseases in several hundred plant species. The inability to culture phytoplasmas <it>in vitro </it>has hindered their characterization at the molecular level. Though the complete genomes of two phytoplasmas have been published, little information has been obtained about the proteome of phytoplasma. Therefore, the proteomic information of phytoplasmas would be useful to elucidate the functional mechanisms of phytoplasma in many biological processes.</p> <p>Results</p> <p>MD phytoplasmas, which belong to the 16SrI-B subgroup based on the 16S DNA analysis, were purified from infected tissues using a combination of differential centrifugation and density gradient centrifugation. The expressed proteome of phytoplasma was surveyed by one-dimensional SDS-PAGE and nanocapillary liquid chromatography-tandem mass spectrometry. A total of 209 phytoplasma proteins were unambiguously assigned, including the proteins with the functions of amino acid biosynthesis, cell envelope, cellular processes, energy metabolism, nucleosides and nucleotide metabolism, replication, transcription, translation, transport and binding as well as the proteins with other functions. In addition to these known function proteins, 63 proteins were annotated as hypothetical or conserved hypothetical proteins.</p> <p>Conclusions</p> <p>Taken together, a total of 209 phytoplasma proteins have been experimentally verified, representing the most extensive survey of any phytoplasma proteome to date. This study provided a valuable dataset of phytoplasma proteins, and a better understanding of the energy metabolism and virulence mechanisms of MD phytoplasma.</p

Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction

User response prediction, which models the user preference w.r.t. the presented items, plays a key role in online services. With two-decade rapid development, nowadays the cumulated user behavior sequences on mature Internet service platforms have become extremely long since the user's first registration. Each user not only has intrinsic tastes, but also keeps changing her personal interests during lifetime. Hence, it is challenging to handle such lifelong sequential modeling for each individual user. Existing methodologies for sequential modeling are only capable of dealing with relatively recent user behaviors, which leaves huge space for modeling long-term especially lifelong sequential patterns to facilitate user modeling. Moreover, one user's behavior may be accounted for various previous behaviors within her whole online activity history, i.e., long-term dependency with multi-scale sequential patterns. In order to tackle these challenges, in this paper, we propose a Hierarchical Periodic Memory Network for lifelong sequential modeling with personalized memorization of sequential patterns for each user. The model also adopts a hierarchical and periodical updating mechanism to capture multi-scale sequential patterns of user interests while supporting the evolving user behavior logs. The experimental results over three large-scale real-world datasets have demonstrated the advantages of our proposed model with significant improvement in user response prediction performance against the state-of-the-arts.Comment: SIGIR 2019. Reproducible codes and datasets: https://github.com/alimamarankgroup/HPM

Growth diagram of La0.7Sr0.3MnO3 thin films using pulsed laser deposition

An experimental study was conducted on controlling the growth mode of La0.7Sr0.3MnO3 thin films on SrTiO3 substrates using pulsed laser deposition (PLD) by tuning growth temperature, pressure and laser fluence. Different thin film morphology, crystallinity and stoichiometry have been observed depending on growth parameters. To understand the microscopic origin, the adatom nucleation, step advance processes and their relationship to film growth were theoretically analyzed and a growth diagram was constructed. Three boundaries between highly and poorly crystallized growth, 2D and 3D growth, stoichiometric and non-stoichiometric growth were identified in the growth diagram. A good fit of our experimental observation with the growth diagram was found. This case study demonstrates that a more comprehensive understanding of the growth mode in PLD is possible

Overestimation of thermal emittance in solenoid scans due to coupled transverse motion

The solenoid scan is a widely used method for the in-situ measurement of the thermal emittance in a photocathode gun. The popularity of this method is due to its simplicity and convenience since all rf photocathode guns are equipped with an emittance compensation solenoid. This paper shows that the solenoid scan measurement overestimates the thermal emittance in the ordinary measurement configuration due to a weak quadrupole field (present in either the rf gun or gun solenoid) followed by a rotation in the solenoid. This coupled transverse dynamics aberration introduces a correlation between the beam's horizontal and vertical motion leading to an increase in the measured 2D transverse emittance, thus the overestimation of the thermal emittance. This effect was systematically studied using both analytic expressions and numerical simulations. These studies were experimentally verified using an L-band 1.6-cell rf photocathode gun with a cesium telluride cathode, which shows a thermal emittance overestimation of 35% with a rms laser spot size of 2.7 mm. The paper concludes by showing that the accuracy of the solenoid scan can be improved by using a quadrupole magnet corrector, consisting of a pair of normal and skew quadrupole magnets.Comment: 12 pages, 13 figure

State Regularized Policy Optimization on Data with Dynamics Shift

In many real-world scenarios, Reinforcement Learning (RL) algorithms are trained on data with dynamics shift, i.e., with different underlying environment dynamics. A majority of current methods address such issue by training context encoders to identify environment parameters. Data with dynamics shift are separated according to their environment parameters to train the corresponding policy. However, these methods can be sample inefficient as data are used \textit{ad hoc}, and policies trained for one dynamics cannot benefit from data collected in all other environments with different dynamics. In this paper, we find that in many environments with similar structures and different dynamics, optimal policies have similar stationary state distributions. We exploit such property and learn the stationary state distribution from data with dynamics shift for efficient data reuse. Such distribution is used to regularize the policy trained in a new environment, leading to the SRPO (\textbf{S}tate \textbf{R}egularized \textbf{P}olicy \textbf{O}ptimization) algorithm. To conduct theoretical analyses, the intuition of similar environment structures is characterized by the notion of homomorphous MDPs. We then demonstrate a lower-bound performance guarantee on policies regularized by the stationary state distribution. In practice, SRPO can be an add-on module to context-based algorithms in both online and offline RL settings. Experimental results show that SRPO can make several context-based algorithms far more data efficient and significantly improve their overall performance.Comment: Preprint. Under Revie