Effect of Particle Spin on Trajectory Deflection and Gravitational Lensing

Spin of a test particle is a fundamental property that can affect its motion in a gravitational field. In this work we consider the effect of particle spin on its deflection angle and gravitational lensing in the equatorial plane of arbitrary stationary and axisymmetric spacetimes. To do this we developed a perturbative method that can be applied to spinning signals with arbitrary asymptotic velocity and takes into account the finite distance effect of the source and the observer. The deflection angle $\Delta\varphi$ and total travel time $\Delta t$ are expressed as (quasi-)power series whose coefficients are polynomials of the asymptotic expansion coefficients of the metric functions. It is found that when the spin and orbital angular momenta are parallel (or antiparallel), the deflection angle is decreased (or increased). Apparent angles $\theta$ of the images in gravitational lensing and their time delays are also solved. In Kerr spacetime, spin affects the apparent angle $\theta_K$ in a way similar to its effect on $\Delta\varphi_K$. The time delay between signals with opposite spins is found to be proportional to the signal spin at leading order. These time delays might be used to constrain the spin to mass ratio of neutrinos.Comment: 33 pages, 7 figures, to match the published versio

Polymers Going Rigid, Thick, and Laterally Infinite

This short review article provides insight into the impact organic chemistry can have on state-of-the-art materials creation. Three cases were selected from the authors' laboratory, the first being 'Suzuki polycondensation', a powerful method with which innovative organic chemistry was successfully transferred to polymer synthesis and which meanwhile has even found technical scale application. The second describes the decoration of linear polymers with a dense layer of regular branch work. Though seemingly a rather esoteric enterprise, these decorations resulted in considerable property changes as compared to other linear polymers and, additionally, led to the discovery of novel properties. Consequently, this area which is commonly referred to as 'dendronized polymers' in a world-wide activity has been developed into a ripe research field, presently under exploration for possible technical application. Finally, an example still very much in its first tumbling steps was selected to give yet another perspective of the role of organic chemistry in a materials-oriented chemistry. It aims at the generation of ultrathin, yet internally strictly defined, sheet-like macromolecules for which a great future is foreseen, e.g. as a 2D platform for the systematic construction of 3D matter

Direct Numerical Simulation of Hypersonic Turbulent Boundary Layer inside an Axisymmetric Nozzle

As a first step toward a study of acoustic disturbance field within a conventional, hypersonic wind tunnel, direct numerical simulations (DNS) of a Mach 6 turbulent boundary layer on the inner wall of a straight axisymmetric nozzle are conducted and the results are compared with those for a flat plate. The DNS results for a nozzle radius to boundary-layer thickness ratio of 5:5 show that the turbulence statistics of the nozzle-wall boundary layer are nearly unaffected by the transverse curvature of the nozzle wall. Before the acoustic waves emanating from different parts of the nozzle surface can interfere with each other and undergo reflections from adjacent portions of the nozzle surface, the rms pressure fluctuation beyond the boundary layer edge increases toward the nozzle axis, apparently due to a focusing effect inside the axisymmetric configuration. Spectral analysis of pressure fluctuations at both the wall and the freestream indicates a similar distribution of energy content for both the nozzle and the flat plate, with the peak of the premultiplied frequency spectrum at a frequency of [(omega)(delta)]/U(sub infinity) approximately 6.0 inside the free stream and at [(omega)(delta)]/U(sub infinity) approximately 2.0 along the wall. The present results provide the basis for follow-on simulations involving reverberation effects inside the nozzle

Can the Copernican principle be tested by cosmic neutrino background?

The Copernican principle, stating that we do not occupy any special place in our universe, is usually taken for granted in modern cosmology. However recent observational data of supernova indicate that we may live in the under-dense center of our universe, which makes the Copernican principle challenged. It thus becomes urgent and important to test the Copernican principle via cosmological observations. Taking into account that unlike the cosmic photons, the cosmic neutrinos of different energies come from the different places to us along the different worldlines, we here propose cosmic neutrino background as a test of the Copernican principle. It is shown that from the theoretical perspective cosmic neutrino background can allow one to determine whether the Copernican principle is valid or not, but to implement such an observation the larger neutrino detectors are called for.Comment: JHEP style, 10 pages, 4 figures, version to appear in JCA

Morphologies and elemental compositions of local biomass burning particles at urban and glacier sites in southeastern Tibetan Plateau: Results from an expedition in 2010

Many studies indicate that the atmospheric environment over the southern part of the Tibetan Plateau is influenced by aged biomass burning particles that are transported over long distances from South Asia. However, our knowledge of the particles emitted locally (within the plateau region) is poor. We collected aerosol particles at four urban sites and one remote glacier site during a scientific expedition to the southeastern Tibetan Plateau in spring 2010. Weather and backward trajectory analyses indicated that the particles we collected were more likely dominated by particles emitted within the plateau. The particles were examined using an electron microscope and identified according to their sizes, shapes and elemental compositions. At three urban sites where the anthropogenic particles were produced mainly by the burning of firewood, soot aggregates were in the majority and made up >40% of the particles by number. At Lhasa, the largest city on the Tibetan Plateau, tar balls and mineral particles were also frequently observed because of the use of coal and natural gas, in addition to biofuel. In contrast, at the glacier site, large numbers of chain-like soot aggregates (similar to 25% by number) were noted. The morphologies of these aggregates were similar to those of freshly emitted ones at the urban sites: moreover, physically or chemically processed ageing was rarely confirmed. These limited observations suggest that the biomass burning particles age slowly in the cold, dry plateau air. Anthropogenic particles emitted locally within the elevated plateau region may thus affect the environment within glaciated areas in Tibet differently than anthropogenic particles transported from South Asia. (C) 2018 Elsevier B.V. All rights reserved

DeepGAR: Deep Graph Learning for Analogical Reasoning

Analogical reasoning is the process of discovering and mapping correspondences from a target subject to a base subject. As the most well-known computational method of analogical reasoning, Structure-Mapping Theory (SMT) abstracts both target and base subjects into relational graphs and forms the cognitive process of analogical reasoning by finding a corresponding subgraph (i.e., correspondence) in the target graph that is aligned with the base graph. However, incorporating deep learning for SMT is still under-explored due to several obstacles: 1) the combinatorial complexity of searching for the correspondence in the target graph; 2) the correspondence mining is restricted by various cognitive theory-driven constraints. To address both challenges, we propose a novel framework for Analogical Reasoning (DeepGAR) that identifies the correspondence between source and target domains by assuring cognitive theory-driven constraints. Specifically, we design a geometric constraint embedding space to induce subgraph relation from node embeddings for efficient subgraph search. Furthermore, we develop novel learning and optimization strategies that could end-to-end identify correspondences that are strictly consistent with constraints driven by the cognitive theory. Extensive experiments are conducted on synthetic and real-world datasets to demonstrate the effectiveness of the proposed DeepGAR over existing methods.Comment: 22nd IEEE International Conference on Data Mining (ICDM 2022

KMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot Node Classification

Recently, Zero-Shot Node Classification (ZNC) has been an emerging and crucial task in graph data analysis. This task aims to predict nodes from unseen classes which are unobserved in the training process. Existing work mainly utilizes Graph Neural Networks (GNNs) to associate features' prototypes and labels' semantics thus enabling knowledge transfer from seen to unseen classes. However, the multi-faceted semantic orientation in the feature-semantic alignment has been neglected by previous work, i.e. the content of a node usually covers diverse topics that are relevant to the semantics of multiple labels. It's necessary to separate and judge the semantic factors that tremendously affect the cognitive ability to improve the generality of models. To this end, we propose a Knowledge-Aware Multi-Faceted framework (KMF) that enhances the richness of label semantics via the extracted KG (Knowledge Graph)-based topics. And then the content of each node is reconstructed to a topic-level representation that offers multi-faceted and fine-grained semantic relevancy to different labels. Due to the particularity of the graph's instance (i.e., node) representation, a novel geometric constraint is developed to alleviate the problem of prototype drift caused by node information aggregation. Finally, we conduct extensive experiments on several public graph datasets and design an application of zero-shot cross-domain recommendation. The quantitative results demonstrate both the effectiveness and generalization of KMF with the comparison of state-of-the-art baselines