Deflection and Gravitational Lensing in Kerr spacetime off equatorial plane

This paper investigates the off-equatorial plane deflection and gravitational lensing of both null and timeline signals in Kerr spacetime in the weak deflection limit, with the finite distance effect of the source and detector taken into account. The deflection in both the Boyer-Linquidist $\phi$ and $\theta$ directions are computed as power series of $M/r_0$ and $r_0/r_{s,d}$, where $M,~r_{s,d}$ are the spacetime mass and source and detector radius respectively, and $r_0$ is the minimal radius of the trajectory. The coefficients of these series are simple trigonometric functions of $\theta_\text{e}$, the extreme value of $\theta$ coordinate of the trajectory. A set of exact gravitational lensing equations is used to solve $r_0,~\theta_\text{e}$ for a given deflection $\delta\theta$ and $\delta\phi$ of the source, and two images are always obtained. The apparent angles and their magnifications of these images are solved and their dependence on various parameters, especially spacetime spin $\hat{a}$ are analyzed in great detail. It is found that generally there exist two critical spacetime spin which separate the signals reaching the detector from different sides of the spin axis and the images to appear either in the first or the second, and the third and the fourth quadrant of the celestial plane. Three potential applications of these results are discussed.Comment: 20 pages; 10 figures, 1 tabl

Multiple-object Grasping Using a Multiple-suction-cup Vacuum Gripper in Cluttered Scenes

Multiple-suction-cup grasping can improve the efficiency of bin picking in cluttered scenes. In this paper, we propose a grasp planner for a vacuum gripper to use multiple suction cups to simultaneously grasp multiple objects or an object with a large surface. To take on the challenge of determining where to grasp and which cups to activate when grasping, we used 3D convolution to convolve the affordable areas inferred by neural network with the gripper kernel in order to find graspable positions of sampled gripper orientations. The kernel used for 3D convolution in this work was encoded including cup ID information, which helps to directly determine which cups to activate by decoding the convolution results. Furthermore, a sorting algorithm is proposed to find the optimal grasp among the candidates. Our planner exhibited good generality and successfully found multiple-cup grasps in previous affordance map datasets. Our planner also exhibited improved picking efficiency using multiple suction cups in physical robot picking experiments. Compared with single-object (single-cup) grasping, multiple-cup grasping contributed to 1.45x, 1.65x, and 1.16x increases in efficiency for picking boxes, fruits, and daily necessities, respectively

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

Learning suction graspability considering grasp quality and robot reachability for bin-picking

Deep learning has been widely used for inferring robust grasps. Although human-labeled RGB-D datasets were initially used to learn grasp configurations, preparation of this kind of large dataset is expensive. To address this problem, images were generated by a physical simulator, and a physically inspired model (e.g., a contact model between a suction vacuum cup and object) was used as a grasp quality evaluation metric to annotate the synthesized images. However, this kind of contact model is complicated and requires parameter identification by experiments to ensure real world performance. In addition, previous studies have not considered manipulator reachability such as when a grasp configuration with high grasp quality is unable to reach the target due to collisions or the physical limitations of the robot. In this study, we propose an intuitive geometric analytic-based grasp quality evaluation metric. We further incorporate a reachability evaluation metric. We annotate the pixel-wise grasp quality and reachability by the proposed evaluation metric on synthesized images in a simulator to train an auto-encoder--decoder called suction graspability U-Net++ (SG-U-Net++). Experiment results show that our intuitive grasp quality evaluation metric is competitive with a physically-inspired metric. Learning the reachability helps to reduce motion planning computation time by removing obviously unreachable candidates. The system achieves an overall picking speed of 560 PPH (pieces per hour).Comment: 18 pages, 2 tables, 7 figure

Chromium deposition and poisoning of La0.8Sr0.2MnO3 oxygen electrodes of solid oxide electrolysis cells

The effect of the presence of an Fe–Cr alloy metallic interconnect on the performance and stability of La0.8Sr0.2MnO3 (LSM) oxygen electrodes is studied for the first time under solid oxide electrolysis cell (SOEC) operating conditions at 800 °C. The presence of the Fe–Cr interconnect accelerates the degradation and delamination processes of the LSM oxygen electrodes. The disintegration of LSM particles and the formation of nanoparticles at the electrode/electrolyte interface are much faster as compared to that in the absence of the interconnect. Cr deposition occurs in the bulk of the LSM oxygen electrode with a high intensity on the YSZ electrolyte surface and on the LSM electrode inner surface close to the electrode/electrolyte interface. SIMS, GI-XRD, EDS and XPS analyses clearly identify the deposition and formation of chromium oxides and strontium chromate on both the electrolyte surface and electrode inner surface. The anodic polarization promotes the surface segregation of SrO and depresses the generation of manganese species such as Mn2+. This is evidently supported by the observation of the deposition of SrCrO4, rather than (Cr,Mn)3O4 spinels as in the case under the operating conditions of solid oxide fuel cells. The present results demonstrate that the Cr deposition is essentially a chemical process, initiated by the nucleation and grain growth reaction between the gaseous Cr species and segregated SrO on LSM oxygen electrodes under SOEC operating conditions

Smooth Pursuit Eye Movements Improve Temporal Resolution for Color Perception

Human observers see a single mixed color (yellow) when different colors (red and green) rapidly alternate. Accumulating evidence suggests that the critical temporal frequency beyond which chromatic fusion occurs does not simply reflect the temporal limit of peripheral encoding. However, it remains poorly understood how the central processing controls the fusion frequency. Here we show that the fusion frequency can be elevated by extra-retinal signals during smooth pursuit. This eye movement can keep the image of a moving target in the fovea, but it also introduces a backward retinal sweep of the stationary background pattern. We found that the fusion frequency was higher when retinal color changes were generated by pursuit-induced background motions than when the same retinal color changes were generated by object motions during eye fixation. This temporal improvement cannot be ascribed to a general increase in contrast gain of specific neural mechanisms during pursuit, since the improvement was not observed with a pattern flickering without changing position on the retina or with a pattern moving in the direction opposite to the background motion during pursuit. Our findings indicate that chromatic fusion is controlled by a cortical mechanism that suppresses motion blur. A plausible mechanism is that eye-movement signals change spatiotemporal trajectories along which color signals are integrated so as to reduce chromatic integration at the same locations (i.e., along stationary trajectories) on the retina that normally causes retinal blur during fixation

Soluble Rhesus Lymphocryptovirus gp350 Protects against Infection and Reduces Viral Loads in Animals that Become Infected with Virus after Challenge

Epstein-Barr virus (EBV) is a human lymphocryptovirus that is associated with several malignancies. Elevated EBV DNA in the blood is observed in transplant recipients prior to, and at the time of post-transplant lymphoproliferative disease; thus, a vaccine that either prevents EBV infection or lowers the viral load might reduce certain EBV malignancies. Two major approaches have been suggested for an EBV vaccine- immunization with either EBV glycoprotein 350 (gp350) or EBV latency proteins (e.g. EBV nuclear antigens [EBNAs]). No comparative trials, however, have been performed. Rhesus lymphocryptovirus (LCV) encodes a homolog for each gene in EBV and infection of monkeys reproduces the clinical, immunologic, and virologic features of both acute and latent EBV infection. We vaccinated rhesus monkeys at 0, 4 and 12 weeks with (a) soluble rhesus LCV gp350, (b) virus-like replicon particles (VRPs) expressing rhesus LCV gp350, (c) VRPs expressing rhesus LCV gp350, EBNA-3A, and EBNA-3B, or (d) PBS. Animals vaccinated with soluble gp350 produced higher levels of antibody to the glycoprotein than those vaccinated with VRPs expressing gp350. Animals vaccinated with VRPs expressing EBNA-3A and EBNA-3B developed LCV-specific CD4 and CD8 T cell immunity to these proteins, while VRPs expressing gp350 did not induce detectable T cell immunity to gp350. After challenge with rhesus LCV, animals vaccinated with soluble rhesus LCV gp350 had the best level of protection against infection based on seroconversion, viral DNA, and viral RNA in the blood after challenge. Surprisingly, animals vaccinated with gp350 that became infected had the lowest LCV DNA loads in the blood at 23 months after challenge. These studies indicate that gp350 is critical for both protection against infection with rhesus LCV and for reducing the viral load in animals that become infected after challenge. Our results suggest that additional trials with soluble EBV gp350 alone, or in combination with other EBV proteins, should be considered to reduce EBV infection or virus-associated malignancies in humans

A redox state-dictated signalling pathway deciphers the malignant cell specificity of CD40-mediated apoptosis

CD40, a member of the tumour necrosis factor receptor (TNFR) superfamily, has the capacity to cause extensive apoptosis in carcinoma cells, while sparing normal epithelial cells. Yet, apoptosis is only achieved by membrane-presented CD40 ligand (mCD40L), as soluble receptor agonists are but weakly pro-apoptotic. Here, for the first time we have identified the precise signalling cascade underpinning mCD40L-mediated death as involving sequential TRAF3 stabilisation, ASK1 phosphorylation, MKK4 (but not MKK7) activation and JNK/AP-1 induction, leading to a Bak- and Bax-dependent mitochondrial apoptosis pathway. TRAF3 is central in the activation of the NADPH oxidase (Nox)-2 component p40phox and the elevation of reactive oxygen species (ROS) is essential in apoptosis. Strikingly, CD40 activation resulted in down-regulation of Thioredoxin (Trx)-1 to permit ASK1 activation and apoptosis. Although soluble receptor agonist alone could not induce death, combinatorial treatment incorporating soluble CD40 agonist and pharmacological inhibition of Trx-1 was functionally equivalent to the signal triggered by mCD40L. Finally, we demonstrate using normal, ‘para-malignant’ and tumour-derived cells that progression to malignant transformation is associated with increase in oxidative stress in epithelial cells, which coincides with increased susceptibility to CD40 killing, while in normal cells CD40 signalling is cytoprotective. Our studies have revealed the molecular nature of the tumour specificity of CD40 signalling and explained the differences in pro-apoptotic potential between soluble and membrane-bound CD40 agonists. Equally importantly, by exploiting a unique epithelial culture system that allowed us to monitor alterations in the redox-state of epithelial cells at different stages of malignant transformation, our study reveals how pro-apoptotic signals can elevate ROS past a previously hypothesised ‘lethal pro-apoptotic threshold’ to induce death; an observation that is both of fundamental importance and carries implications for cancer therap

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve