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The importance of Very Long Baseline Interferometry (VLBI) for pulsar research is becoming increasingly prominent and receiving more and more attention. In this paper, we present pathfinding pulsar observation results with the Chinese VLBI Network (CVN) incorporating the Five-hundred-meter Aperture Spherical radio Telescope (FAST). On MJD 60045 (April 11th, 2023), PSRs B0919+06 and B1133+16 were observed with the phase-referencing mode in the L-band using four radio telescopes (FAST, TianMa, Haoping and Nanshan) and correlated with the pulsar binning mode of the distributed FX-style software correlator in Shanghai. After further data processing with the NRAO Astronomical Image Processing System (AIPS), we detected these two pulsars and fitted their current positions with accuracy at the milliarcsecond level. By comparison, our results show significantly better agreement with predicted values based on historical VLBI observations than that with previous timing observations, as pulsar astrometry with the VLBI provides a more direct and model-independent method for accurately obtaining related parameters., Comment: Accepted by the Chinese Physics Letters

Electric vehicles (EVs) can be aggregated to offer flexibility services to the power system. However, the rapid growth in EV adoption leads to increased grid-level carbon emissions due to higher EV charging demand, complicating grid decarbonization efforts. Quantifying and managing EV flexibility while controlling carbon emissions is crucial. This paper introduces a methodology for carbon-aware quantification of real-time aggregate EV power flexibility. An offline model is first developed to determine the upper and lower bounds of the EV flexibility region. To address uncertainties in EV charging behaviors and grid carbon intensity, we propose a carbon-aware online optimization algorithm based on Lyapunov optimization, incorporating a queue model to capture system dynamics. To enhance EV flexibility, we integrate dispatch signals from the system operator into the queue update through a two-stage disaggregation process. The proposed approach is prediction-free and adaptable to various uncertainties. Additionally, the maximum charging delay for EV charging tasks is theoretically bounded by a constant, and carbon emissions are effectively controlled. Numerical results demonstrate the effectiveness of the proposed online method and highlight its advantages over several benchmarks through comparisons., Comment: 12 pages, 10 figures

Coherent supercontinuum radiation in the extreme-ultraviolet (XUV) range is indispensable for synthesizing attosecond light pulses and for exploring transient atomic structures. Here, we report the striking observations of coherent XUV supercontinuum (XSC) extended from below to far above the ionization threshold, which exhibits completely different temporal and spatial properties comparing to the conventional rescattering induced high harmonic generation (HHG). We demonstrate that the strong-field created coherence among bound orbitals strongly distort the atomic transition energies during the pulse, leading to coherent emission spanning tens of electron-volts, in contrast to the line emission via free-induction decay occurring after the pulse. The supposed non-radiating bound dark states contribute as well by emitting dressed energy through dark-to-bright emission mechanism. All the processes modulated at sub-cycle time scale jointly form this new-type coherent XSC. This work achieves the strong-field attosecond control of the exotic atomic radiation dynamics and provides the means of simultaneous generation of separated attosecond sources, i.e., XSC and HHG, with potential advancing attosecond interferometry.

The coupled electronic-vibrational dynamics of nitrogen ions induced by strong-field ionization is investigated theoretically to corroborate the recent transient X-ray K-edge absorption experiment [PRL 129, 123002 (2022)], where the population distribution of three electronic states in air lasing of N2+ was determined for the first time. By extending the ionization-coupling model to include the transient absorption, we successfully reproduce the time-resolved X-ray absorption spectra of nitrogen ions observed in the experiment. By identifying the contributions from different electronic states, the study provides different interpretation revealing the significant role of excited state A arising from the strong coupling between vibrational states in strong laser fields. It indicates that the electronic population inversion occurs at least for certain alignment of nitrogen molecules. The theory helps uncovering new features of absorption from forbidden transitions during ionization and confirming that the vibration coherence at each electronic channel induces the modulation of absorbance after strong field ionization. A new scheme is proposed to determine the population transfer at different probing geometry to avoid the spectral overlap. This work offers valuable insights into the intricate interplay between electronic and vibrational dynamics and helps to resolve the debate on nitrogen air lasing.

Graph Neural Networks (GNNs) have demonstrated promising results on exploiting node representations for many downstream tasks through supervised end-to-end training. To deal with the widespread label scarcity issue in real-world applications, Graph Contrastive Learning (GCL) is leveraged to train GNNs with limited or even no labels by maximizing the mutual information between nodes in its augmented views generated from the original graph. However, the distribution of graphs remains unconsidered in view generation, resulting in the ignorance of unseen edges in most existing literature, which is empirically shown to be able to improve GCL's performance in our experiments. To this end, we propose to incorporate graph generative adversarial networks (GANs) to learn the distribution of views for GCL, in order to i) automatically capture the characteristic of graphs for augmentations, and ii) jointly train the graph GAN model and the GCL model. Specifically, we present GACN, a novel Generative Adversarial Contrastive learning Network for graph representation learning. GACN develops a view generator and a view discriminator to generate augmented views automatically in an adversarial style. Then, GACN leverages these views to train a GNN encoder with two carefully designed self-supervised learning losses, including the graph contrastive loss and the Bayesian personalized ranking Loss. Furthermore, we design an optimization framework to train all GACN modules jointly. Extensive experiments on seven real-world datasets show that GACN is able to generate high-quality augmented views for GCL and is superior to twelve state-of-the-art baseline methods. Noticeably, our proposed GACN surprisingly discovers that the generated views in data augmentation finally conform to the well-known preferential attachment rule in online networks., Comment: KDD 2023

We develop the moduli theory of boundary polarized CY pairs, which are slc Calabi-Yau pairs $(X,D)$ such that $D$ is ample. The motivation for studying this moduli problem is to construct a moduli space at the Calabi-Yau wall interpolating between certain K-moduli and KSBA moduli spaces. We prove that the moduli stack of boundary polarized CY pairs is S-complete, $\Theta$-reductive, and satisfies the existence part of the valuative criterion for properness, which are steps towards constructing a proper moduli space. A key obstacle in this theory is that the irreducible components of the moduli stack are not in general of finite type. Despite this issue, in the case of pairs $(X,D)$ where $X$ is a degeneration of $\mathbb{P}^2$, we construct a projective moduli space on which the Hodge line bundle is ample. As a consequence, we complete the proof of a conjecture of Prokhorov and Shokurov in relative dimension two., Comment: 105 pages, comments are very welcome

Recommender systems are able to learn user preferences based on user and item representations via their historical behaviors. To improve representation learning, recent recommendation models start leveraging information from various behavior types exhibited by users. In real-world scenarios, the user behavioral graph is not only multiplex but also dynamic, i.e., the graph evolves rapidly over time, with various types of nodes and edges added or deleted, which causes the Neighborhood Disturbance. Nevertheless, most existing methods neglect such streaming dynamics and thus need to be retrained once the graph has significantly evolved, making them unsuitable in the online learning environment. Furthermore, the Neighborhood Disturbance existing in dynamic graphs deteriorates the performance of neighbor-aggregation based graph models. To this end, we propose SUPA, a novel graph neural network for dynamic multiplex heterogeneous graphs. Compared to neighbor-aggregation architecture, SUPA develops a sample-update-propagate architecture to alleviate neighborhood disturbance. Specifically, for each new edge, SUPA samples an influenced subgraph, updates the representations of the two interactive nodes, and propagates the interaction information to the sampled subgraph. Furthermore, to train SUPA incrementally online, we propose InsLearn, an efficient workflow for single-pass training of large dynamic graphs. Extensive experimental results on six real-world datasets show that SUPA has a good generalization ability and is superior to sixteen state-of-the-art baseline methods. The source code is available at https://github.com/shatter15/SUPA., Comment: ICDE 2023

Modern recommender systems often deal with a variety of user interactions, e.g., click, forward, purchase, etc., which requires the underlying recommender engines to fully understand and leverage multi-behavior data from users. Despite recent efforts towards making use of heterogeneous data, multi-behavior recommendation still faces great challenges. Firstly, sparse target signals and noisy auxiliary interactions remain an issue. Secondly, existing methods utilizing self-supervised learning (SSL) to tackle the data sparsity neglect the serious optimization imbalance between the SSL task and the target task. Hence, we propose a Multi-Behavior Self-Supervised Learning (MBSSL) framework together with an adaptive optimization method. Specifically, we devise a behavior-aware graph neural network incorporating the self-attention mechanism to capture behavior multiplicity and dependencies. To increase the robustness to data sparsity under the target behavior and noisy interactions from auxiliary behaviors, we propose a novel self-supervised learning paradigm to conduct node self-discrimination at both inter-behavior and intra-behavior levels. In addition, we develop a customized optimization strategy through hybrid manipulation on gradients to adaptively balance the self-supervised learning task and the main supervised recommendation task. Extensive experiments on five real-world datasets demonstrate the consistent improvements obtained by MBSSL over ten state-of-the art (SOTA) baselines. We release our model implementation at: https://github.com/Scofield666/MBSSL.git., Comment: SIGIR 2023