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