Applying Bayesian Neural Networks to Event Reconstruction in Reactor Neutrino Experiments

A toy detector has been designed to simulate central detectors in reactor neutrino experiments in the paper. The electron samples from the Monte-Carlo simulation of the toy detector have been reconstructed by the method of Bayesian neural networks (BNN) and the standard algorithm, a maximum likelihood method (MLD), respectively. The result of the event reconstruction using BNN has been compared with the one using MLD. Compared to MLD, the uncertainties of the electron vertex are not improved, but the energy resolutions are significantly improved using BNN. And the improvement is more obvious for the high energy electrons than the low energy ones.Comment: 9 pages, 3 figures, Accepted by NIM

The Radiation Structure of PSR B2016++28 Observed with FAST

With the largest dish Five-hundred-meter Aperture Spherical radio Telescope (FAST), both the mean and single pulses of PSR B2016$+$28, especially including the single-pulse structure, are investigated in detail in this study. The mean pulse profiles at different frequencies can be well fitted in a conal model, and the peak separation of intensity-dependent pulse profiles increases with intensity. The integrated pulses are obviously frequency dependent (pulse width decreases by $\sim\,20\%$ as frequency increases from 300 MHz to 750 MHz), but the structure of single pulses changes slightly (the corresponding correlation scale decreases by only $\sim\,1\%$). This disparity between mean and single pulses provides independent evidence for the existence of the RS-type vacuum inner gap, indicating a strong bond between particles on the pulsar surface. Diffused drifting sub-pulses are analyzed. The results show that the modulation period along pulse series ($P_3$) is positively correlated to the separation between two adjacent sub-pulses ($P_2$). This correlation may hint a rough surface on the pulsar, eventually resulting in the irregular drift of sparks. All the observational results may have significant implications in the dynamics of pulsar magnetosphere and are discussed extensively in this paper.Comment: Sci. China-Phys. Mech. Astron. 62, 959505 (2019

A triple-band terahertz metamaterial absorber based on buck Dirac semimetals

In this paper, a triple-band terahertz metamaterial absorber (MA) based on buck Dirac semimetals (BDSs) is proposed, which consists of a windmill-shaped element in a square ring layer, a dielectric layer, and a BDSs layer. The MA unit cell is investigated at normal and oblique incidence angle for both transverse electric (TE) and transverse magnetic (TM) polarizations. The simulation results show that the MA has three high absorption peaks at 0.80 THz, 1.72 THz, and 3.38 THz. The corresponding peak absorbance are 99.43%, 99.92% and 99.58%, respectively. Moreover, the absorption peaks of MA can be tuned by adjusting the Fermi energy of the BDSs. And the density of electric field, the magnetic field, and surface current distributions of the MA are given to reveal the absorption mechanism. According to the simulation results, the designed MA not only has high absorbance, but also insensitive to polarizations. Hence it is favorable for various applications, such as terahertz detecting, radar stealth and bio-chemical sensor. Keywords: BDSs, Terahertz, Perfect absorber, Polarization-independen

Automatic Insertion of Hot Keywords to Drive Traffic on Advertisements

Product titles and descriptions that include appropriate keywords, when used in an online advertisement, can improve the shopping feed quality and resultant traffic to the advertiser. However, online merchants lack knowledge of currently trending or popular keywords, and lacking keyword ideation, may choose suboptimal product titles. This disclosure describes techniques that enable online merchants to automatically optimize product titles or descriptions, e.g., as used in online ads. Trending or popular keywords relevant to the product are automatically added to the product title or description. Unique, product-specific insights gleaned from searched terms are utilized to improve title effectiveness automatically and at scale

Revisiting the Design Patterns of Composite Visualizations

Composite visualization is a popular design strategy that represents complex datasets by integrating multiple visualizations in a meaningful and aesthetic layout, such as juxtaposition, overlay, and nesting. With this strategy, numerous novel designs have been proposed in visualization publications to accomplish various visual analytic tasks. These well-crafted composite visualizations have formed a valuable collection for designers and researchers to address real-world problems and inspire new research topics and designs. However, there is a lack of understanding of design patterns of composite visualization, thus failing to provide holistic design space and concrete examples for practical use. In this paper, we opted to revisit the composite visualizations in VIS publications and answered what and how visualizations of different types are composed together. To achieve this, we first constructed a corpus of composite visualizations from IEEE VIS publications and decomposed them into a series of basic visualization types (e.g., bar chart, map, and matrix). With this corpus, we studied the spatial (e.g., separated or overlaying) and semantic relationships (e.g., with same types or shared axis) between visualizations and proposed a taxonomy consisting of eight different design patterns (e.g., repeated, stacked, accompanied, and nested). Furthermore, we analyzed and discussed common practices of composite visualizations, such as the distribution of different patterns and correlations between visualization types. From the analysis and examples, we obtained insights into different design patterns on the utilities, advantages, and disadvantages. Finally, we developed an interactive system to help visualization developers and researchers conveniently explore collected examples and design patterns