2,559 research outputs found
The localization of single pulse in VLBI observation
In our previous work, we propose a cross spectrum based method to extract
single pulse signals from RFI contaminated data, which is originated from
geodetic VLBI postprocessing. This method fully utilizes fringe phase
information of the cross spectrum and hence maximizes signal power, however the
localization was not discussed in that work yet. As the continuation of that
work, in this paper, we further study how to localize single pulses using
astrometric solving method. Assuming that the burst is a point source, we
derive the burst position by solving a set of linear equations given the
relation between residual delay and offset to a priori position. We find that
the single pulse localization results given by both astrometric solving and
radio imaging are consistent within 3 sigma level. Therefore we claim that it
is possible to derive the position of a single pulse with reasonable precision
based on only 3 or even 2 baselines with 4 milliseconds integration. The
combination of cross spectrum based detection and the localization proposed in
this work then provide a thorough solution for searching single pulse in VLBI
observation. According to our calculation, our pipeline gives comparable
accuracy as radio imaging pipeline. Moreover, the computational cost of our
pipeline is much smaller, which makes it more practical for FRB search in
regular VLBI observation. The pipeline is now publicly available and we name it
as "VOLKS", which is the acronym of "VLBI Observation for frb Localization Keen
Searcher".Comment: 11 pages, 4 figures, 3 tables, accepted for publication in A
Coercivity Mechanisms of Single-Molecule Magnets
Magnetic hysteresis has become a crucial aspect for characterizing
single-molecule magnets, but the comprehension of the coercivity mechanism is
still a challenge. By using analytical derivation and quantum dynamical
simulations, we reveal fundamental rules that govern magnetic relaxation of
single molecule magnets under the influence of external magnetic fields, which
in turn dictates the hysteresis behavior. Specifically, we find that energy
level crossing induced by magnetic fields can drastically increase the
relaxation rate and set a coercivity limit. The activation of
optical-phonon-mediated quantum tunneling accelerates the relaxation and
largely determines the coercivity. Intra-molecular exchange interaction in
multi-ion compounds may enhance the coercivity by suppressing key relaxation
processes. A single-occupant bond in mixed-valence complexes compromises
coercivity, and pre-spin-flip of the bonding electron facilitates the overall
magnetization reversal. Underlying these properties are magnetic relaxation
processes modulated by the interplay of magnetic fields, phonon spectrum and
spin state configuration, which also proposes a fresh perspective for the
nearly centurial coercive paradox.Comment: 18 pages, 3 figure
Measuring the boundary gapless state and criticality via disorder operator
The disorder operator is often designed to reveal the conformal field theory
(CFT) information in the quantum many-body system. By using large-scale quantum
Monte Carlo simulation, we study the scaling behavior of disorder operator on
the boundary in the two-dimensional Heisenberg model on the square-octagon
lattice with gapless topological edge state. In the Affleck-Kennedy-Lieb-Tasaki
(AKLT) phase, the disorder operator is shown to hold the perimeter scaling with
a logarithmic term associated with the Luttinger Liquid parameter K. This
effective Luttinger Liquid parameter K reflects the low energy physics and CFT
for (1+1)d boundary. At bulk critical point, the effective K is suppressed but
keep finite value, indicating the coupling between the gapless edge state and
bulk fluctuation. The logarithmic term numerically capture this coupling
picture, which reveals the (1+1)d SU(2)_1 CFT and (2+1)d O(3) CFT at boundary
criticality. Our work paves a new way to study the exotic boundary state and
boundary criticality.Comment: 8 Pages,7 figure
Antibiotic resistance in urban green spaces mirrors the pattern of industrial distribution
Urban green spaces are closely related to the activities and health of urban residents. Turf grass and soil are two major interfaces between the environmental and human microbiome, which represent potential pathways for the
spread of antibiotic resistance genes (ARGs) from environmental to human microbiome through skin-surface contact. However, the information regarding the prevalence of ARGs in urban green spaces and drivers in shaping their distribution patterns remain unclear. Here, we profiled a wide spectrum of ARGs in grass phyllosphere and soils from 40 urban parks across Greater Melbourne, Australia, using high throughput quantitative PCR. A total of 217 and 218 unique ARGs and MGEs were detected in grass phyllosphere and soils, respectively,
conferring resistance to almost all major classes of antibiotics commonly used in human and animals. The plant microbiome contained a core resistome, which occupied > 84% of the total abundance of ARGs. In contrast, no core resistome was identified in the soil microbiome. The difference between plant and soil resistome composition was attributed to the difference in bacterial community structure and intensity of environmental and anthropogenic influence. Most importantly, the abundance of ARGs in urban green spaces was significantly
positively related to industrial factors including total number of business, number of manufacturing, and number of electricity, gas, water and waste services in the region. Structural equation models further revealed that
industrial distribution was a major factor shaping the ARG profiles in urban green spaces after accounting for multiple drivers. These findings have important implications for mitigation of the potential risks posed by ARGs
to urban residents
μ-Adipato-bis[chlorido(2,2′:6′,2′′-terpyridine)copper(II)] tetrahydrate
In the title compound, [Cu2(C6H8O4)Cl2(C15H11N3)2]·4H2O, the dinuclear copper complex is located on a crystallographic inversion centre. Each Cu atom is in a distorted square-pyramidal coordination environment, with one O atom of an adipate dianion and three N atoms from the 2,2′:6′,2′′-terpyridine ligand occupying the basal plane, and one chlorine in the apical site. In addition, there is weak Cu—O interaction opposite of the chlorine with a distance of 2.768 (1) Å. The adipate ligand adopts a gauche–anti–gauche conformation. The interstitial water molecules form hydrogen-bonded tertramers that are connected to the complexes via O—H⋯O and O—H⋯Cl hydrogen bonds, thus leading to the formation of tightly hydrogen-bonded layers extending perpendicular to the b-axis direction
PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling
Raw point clouds data inevitably contains outliers or noise through
acquisition from 3D sensors or reconstruction algorithms. In this paper, we
present a novel end-to-end network for robust point clouds processing, named
PointASNL, which can deal with point clouds with noise effectively. The key
component in our approach is the adaptive sampling (AS) module. It first
re-weights the neighbors around the initial sampled points from farthest point
sampling (FPS), and then adaptively adjusts the sampled points beyond the
entire point cloud. Our AS module can not only benefit the feature learning of
point clouds, but also ease the biased effect of outliers. To further capture
the neighbor and long-range dependencies of the sampled point, we proposed a
local-nonlocal (L-NL) module inspired by the nonlocal operation. Such L-NL
module enables the learning process insensitive to noise. Extensive experiments
verify the robustness and superiority of our approach in point clouds
processing tasks regardless of synthesis data, indoor data, and outdoor data
with or without noise. Specifically, PointASNL achieves state-of-the-art robust
performance for classification and segmentation tasks on all datasets, and
significantly outperforms previous methods on real-world outdoor SemanticKITTI
dataset with considerate noise. Our code is released through
https://github.com/yanx27/PointASNL.Comment: To appear in CVPR 2020. Also seen in
http://kaldir.vc.in.tum.de/scannet_benchmark
LATR: 3D Lane Detection from Monocular Images with Transformer
3D lane detection from monocular images is a fundamental yet challenging task
in autonomous driving. Recent advances primarily rely on structural 3D
surrogates (e.g., bird's eye view) built from front-view image features and
camera parameters. However, the depth ambiguity in monocular images inevitably
causes misalignment between the constructed surrogate feature map and the
original image, posing a great challenge for accurate lane detection. To
address the above issue, we present a novel LATR model, an end-to-end 3D lane
detector that uses 3D-aware front-view features without transformed view
representation. Specifically, LATR detects 3D lanes via cross-attention based
on query and key-value pairs, constructed using our lane-aware query generator
and dynamic 3D ground positional embedding. On the one hand, each query is
generated based on 2D lane-aware features and adopts a hybrid embedding to
enhance lane information. On the other hand, 3D space information is injected
as positional embedding from an iteratively-updated 3D ground plane. LATR
outperforms previous state-of-the-art methods on both synthetic Apollo,
realistic OpenLane and ONCE-3DLanes by large margins (e.g., 11.4 gain in terms
of F1 score on OpenLane). Code will be released at
https://github.com/JMoonr/LATR .Comment: Accepted by ICCV2023 (Oral
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