arXiv subjects

G. Lin

Publications and source records attributed to G. Lin.

4 recordsLinked to original sources

arXiv2021

The orbital effect on the anomalous magnetism and evolution in La$_x$Y$_{1-x}$VO$_3$($0\le x \le 0.2$) single crystals

The orbital effect on the anomalous magnetism and evolution of low La-doping single crystals, La$_x$Y$_{1-x}$VO$_{3}$ (x = 0, 0.1, and 0.2), has been investigated by applying the X-ray diffraction, specific heat, magnetization and Raman scattering techniques. The larger nearest-neighbor (NN) exchange interaction along c-axis stabilizes the fluctuant G-type orbital ordering (G-OO) which favors the exotic C-type antiferromagnetic order (C-AF). It is found that the NN exchange interaction in ab plane is anisotropy relating to the in plane magnetic anisotropy, which becomes smaller in high La-doped sample. Most interestingly, with increasing the La$^{3+}$ content the orbital fluctuation and hybridization are decreased which stabilizes the C-OO phase and destabilizes the G-OO phase. Meanwhile, the diamagnetism in the exotic C-AF phase becomes weak and the possible mechanism relates to the change of the competition between the single-ion magnetic anisotropy and the Dzyaloshinsky-Moriya (DM) interaction with increasing x. Finally, the strong spin-orbital coupling has been observed at temperature just above T$_N$ in La$_{0.2}$Y$_{0.8}$VO$_{3}$ and a short range spin-orbital correlation is suggested.

cond-mat.str-el↗

arXiv2017

Semantic Segmentation from Limited Training Data

We present our approach for robotic perception in cluttered scenes that led to winning the recent Amazon Robotics Challenge (ARC) 2017. Next to small objects with shiny and transparent surfaces, the biggest challenge of the 2017 competition was the introduction of unseen categories. In contrast to traditional approaches which require large collections of annotated data and many hours of training, the task here was to obtain a robust perception pipeline with only few minutes of data acquisition and training time. To that end, we present two strategies that we explored. One is a deep metric learning approach that works in three separate steps: semantic-agnostic boundary detection, patch classification and pixel-wise voting. The other is a fully-supervised semantic segmentation approach with efficient dataset collection. We conduct an extensive analysis of the two methods on our ARC 2017 dataset. Interestingly, only few examples of each class are sufficient to fine-tune even very deep convolutional neural networks for this specific task.

cs.RO↗

arXiv2009

Perspectives of a Midrapidity Dimuon Program at RHIC: A Novel and Compact Muon Telescope Detector

We propose a large-area, cost-effective Muon Telescope Detector (MTD) for the Solenoidal Tracker at RHIC (STAR) at mid-rapidity and for the next generation of detectors at a possible electron-ion collider. We utilize Multi-gap Resistive Plate Chambers with large modules and long readout strips (Long-MRPC) in the detector design. The results from cosmic ray and beam tests show the intrinsic timing and spatial resolution for a Long-MRPC are 60-70 ps and $\sim1$ cm, respectively. The prototype performance of such a novel muon telescope detector at STAR indicates that muon identification at the transverse momentum of a few GeV/$c$ can be achieved through the combined information of track matching with the MTD, ionization energy loss in the Time Projection Chamber, and time-of-flight measurements. A primary muon over secondary muon ratio of better than 1/3 can be achieved. This provides a promising device for future quarkonium programs and primordial dilepton measurements at RHIC. Simulations of the muon efficiency, the signal-to-background ratio of $J/\psi$, the separation of $\Upsilon$ 1S from 2S+3S states, and the electron-muon correlation from charm pair production in the RHIC environment are presented.

nucl-ex↗

arXiv1993

Renormalization Group and Asymptotics of Solutions of Nonlinear Parabolic Equations

We present a general method for studying long time asymptotics of nonlinear parabolic partial differential equations. The method does not rely on a priori estimates such as the maximum principle. It applies to systems of coupled equations, to boundary conditions at infinity creating a front, and to higher (possibly fractional) differential linear terms. We present in detail the analysis for nonlinear diffusion-type equations with initial data falling off at infinity and also for data interpolating between two different stationary solutions at infinity.

chao-dyn↗