Multiwavelength Analysis of a Nearby Heavily Obscured AGN in NGC 449

We presented the multiwavelength analysis of a heavily obscured active galactic nucleus (AGN) in NGC 449. We first constructed a broadband X-ray spectrum using the latest NuSTAR and XMM-Newton data. Its column density ($\simeq 10^{24} \rm{cm}^{-2}$) and photon index ($\Gamma\simeq 2.4$) were reliably obtained by analyzing the broadband X-ray spectrum. However, the scattering fraction and the intrinsic X-ray luminosity could not be well constrained. Combined with the information obtained from the mid-infrared (mid-IR) spectrum and spectral energy distribution (SED) fitting, we derived its intrinsic X-ray luminosity ($\simeq 8.54\times 10^{42} \ \rm{erg\ s}^{-1}$) and scattering fraction ($f_{\rm{scat}}\simeq 0.26\%$). In addition, we also derived the following results: (1). The mass accretion rate of central AGN is about $2.54 \times 10^{-2} \rm{M}_\odot\ \rm{yr}^{-1}$, and the Eddington ratio is $8.39\times 10^{-2}$; (2). The torus of this AGN has a high gas-to-dust ratio ($N_{\rm H}/A_{\rm V}=8.40\times 10^{22}\ \rm{cm}^{-2}\ \rm{mag}^{-1}$); (3). The host galaxy and the central AGN are both in the early stage of co-evolution.Comment: 12 pages, 5 figures, 3 tables, Accepted to PAS

NeutronOrch: Rethinking Sample-based GNN Training under CPU-GPU Heterogeneous Environments

Graph Neural Networks (GNNs) have demonstrated outstanding performance in various applications. Existing frameworks utilize CPU-GPU heterogeneous environments to train GNN models and integrate mini-batch and sampling techniques to overcome the GPU memory limitation. In CPU-GPU heterogeneous environments, we can divide sample-based GNN training into three steps: sample, gather, and train. Existing GNN systems use different task orchestrating methods to employ each step on CPU or GPU. After extensive experiments and analysis, we find that existing task orchestrating methods fail to fully utilize the heterogeneous resources, limited by inefficient CPU processing or GPU resource contention. In this paper, we propose NeutronOrch, a system for sample-based GNN training that incorporates a layer-based task orchestrating method and ensures balanced utilization of the CPU and GPU. NeutronOrch decouples the training process by layer and pushes down the training task of the bottom layer to the CPU. This significantly reduces the computational load and memory footprint of GPU training. To avoid inefficient CPU processing, NeutronOrch only offloads the training of frequently accessed vertices to the CPU and lets GPU reuse their embeddings with bounded staleness. Furthermore, NeutronOrch provides a fine-grained pipeline design for the layer-based task orchestrating method, fully overlapping different tasks on heterogeneous resources while strictly guaranteeing bounded staleness. The experimental results show that compared with the state-of-the-art GNN systems, NeutronOrch can achieve up to 11.51x performance speedup

PGC 38025: A Star-forming Lenticular Galaxy With an Off-nuclear Star-forming Core

Lenticular galaxies (S0s) were considered mainly as passive evolved spirals due to environmental effects for a long time; however, most S0s in the field cannot fit into this common scenario. In this work, we study one special case, SDSS J120237.07+642235.3 (PGC 38025), a star-forming field S0 galaxy with an off-nuclear blue core. We present optical integral field spectroscopic (IFS) observation with the 3.5 meter telescope at Calar Alto (CAHA) Observatory, and high-resolution millimeter observation with the NOrthern Extended Millimeter Array (NOEMA). We estimated the star formation rate (SFR = 0.446 $M_\odot yr^{-1}$) and gaseous metallicity (12 + log(O/H) = 8.42) for PGC 38025, which follows the star formation main sequence and stellar mass - metallicity relation. We found that the ionized gas and cold molecular gas in PGC 38025 show the same spatial distribution and kinematics, whilst rotating misaligned with stellar component. The off-nuclear blue core is locating at the same redshift as PGC 38025 and its optical spectrum suggest it is \rm H\,{\sc ii} region. We suggest that the star formation in PGC 38025 is triggered by a gas-rich minor merger, and the off-nuclear blue core might be a local star-formation happened during the accretion/merger process.Comment: 16 pages, 13 figures, accepted for publication in Ap

NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams

Existing Graph Neural Network (GNN) training frameworks have been designed to help developers easily create performant GNN implementations. However, most existing GNN frameworks assume that the input graphs are static, but ignore that most real-world graphs are constantly evolving. Though many dynamic GNN models have emerged to learn from evolving graphs, the training process of these dynamic GNNs is dramatically different from traditional GNNs in that it captures both the spatial and temporal dependencies of graph updates. This poses new challenges for designing dynamic GNN training frameworks. First, the traditional batched training method fails to capture real-time structural evolution information. Second, the time-dependent nature makes parallel training hard to design. Third, it lacks system supports for users to efficiently implement dynamic GNNs. In this paper, we present NeutronStream, a framework for training dynamic GNN models. NeutronStream abstracts the input dynamic graph into a chronologically updated stream of events and processes the stream with an optimized sliding window to incrementally capture the spatial-temporal dependencies of events. Furthermore, NeutronStream provides a parallel execution engine to tackle the sequential event processing challenge to achieve high performance. NeutronStream also integrates a built-in graph storage structure that supports dynamic updates and provides a set of easy-to-use APIs that allow users to express their dynamic GNNs. Our experimental results demonstrate that, compared to state-of-the-art dynamic GNN implementations, NeutronStream achieves speedups ranging from 1.48X to 5.87X and an average accuracy improvement of 3.97%.Comment: 12 pages, 15 figure

Modeling User Viewing Flow using Large Language Models for Article Recommendation

This paper proposes the User Viewing Flow Modeling (SINGLE) method for the article recommendation task, which models the user constant preference and instant interest from user-clicked articles. Specifically, we employ a user constant viewing flow modeling method to summarize the user's general interest to recommend articles. We utilize Large Language Models (LLMs) to capture constant user preferences from previously clicked articles, such as skills and positions. Then we design the user instant viewing flow modeling method to build interactions between user-clicked article history and candidate articles. It attentively reads the representations of user-clicked articles and aims to learn the user's different interest views to match the candidate article. Our experimental results on the Alibaba Technology Association (ATA) website show the advantage of SINGLE, which achieves 2.4% improvements over previous baseline models in the online A/B test. Our further analyses illustrate that SINGLE has the ability to build a more tailored recommendation system by mimicking different article viewing behaviors of users and recommending more appropriate and diverse articles to match user interests.Comment: 8 pages

Transport evidence for the surface state and spin-phonon interaction in FeTe0.5_{0.5}Se0.5_{0.5}

The iron chalcogenides have been proved to be intrinsic topological superconductors to implement quantum computation because of their unique electronic structures. The topologically nontrivial surface states of FeTe$_{0.5}$Se$_{0.5}$ have been predicted by several calculations and then confirmed by high-resolution photoemission and scanning tunneling experiments. However, so far, the shreds of the electrical transport evidence for topological surface states are still in absence. By carrying out electrical transport experiments, we observe a topological transition with a nonlinear Hall conductivity and simultaneous linear magnetoresistance near the superconducting transition temperature. Furthermore, we observe a sign reversal of the Hall coefficient accompanied by a concurrently softening of the ${A}_{1g}$ phonon mode at about 40 K, indicating a nematic transition. The synchronized phonon softening with nematicity manifests an enhanced fluctuation state through spin-phonon interaction. Our results solidly corroborate the topological surface states of FeTe$_{0.5}$Se$_{0.5}$ and provide an understanding of the mechanism of the superconductivity in iron chalcogenides.Comment: 10 pages, 8 figure