arXiv subjects

Jiahua Wu

Publications and source records attributed to Jiahua Wu.

10 recordsLinked to original sources

arXiv2026

A systematic study of the long-term mid-infrared color variations of Seyfert 1 galaxies

We present a comparative analysis of long-term mid-infrared (MIR) color variations (MCVs) for 1,977 narrow-line Seyfert 1 (NLSy1) and 4,113 broad-line Seyfert 1 (BLSy1) galaxies at $z < 0.3$. Using 14 years of WISE and NEOWISE data, we quantify the correlation between $W1-W2$ color and $W1$ magnitude. We categorize sources into bluer-when-brighter (BWB), redder-when-brighter (RWB), and weak/no MCV populations. Our results show that the MCV properties of BLSy1s are consistent with those of NLSy1s. This suggests that host-contaminated dust reprocessing is a universal mechanism across Seyfert 1s. Bolometric luminosity ($\Lbol$) is the primary driver of MCV behavior. However, at fixed $\Lbol$, the fraction of RWB sources decreases with increasing Eddington ratio ($\REdd$). This reveals a secondary dependence on the accretion state. Ensemble structure function analysis further shows that higher-$\REdd$ sources exhibit flatter structure functions at a given $\Lbol$, indicating smaller dust torus radii. These findings suggest that the central engine's radiation and accretion state shape the circumnuclear dust geometry. Radio-loud sources follow the same trends as radio-quiet counterparts, implying that jet emission does not significantly impact long-term MIR variability. Finally, modified blackbody modeling indicates that grains with sizes $a \gtrsim 0.1~μ$m are responsible for the observed MCVs. Our results establish a unified framework for MCVs in Seyfert~1s, where the Eddington ratio modulates both variability patterns and the physical scale of the dust torus. Furthermore, given the similarity between the MCV distributions of CLAGNs and RWB sources, we estimate that $\lesssim 6\%$ of Seyfert~1s may be CLAGN candidates, offering a potential MIR-based pre-selection.

astro-ph.GA↗

arXiv2026

Mrk 382: A Narrow-line Seyfert 1 Galaxy with Recurrent X-ray State Transitions

We report recurrent X-ray state transitions in the nearby narrow-line Seyfert~1 galaxy Mrk~382 using multi-epoch observations from \textit{Swift}, \textit{Chandra}, \textit{XMM-Newton}, and eROSITA, together with archival ultraviolet, optical, and infrared data. The 0.3--2 keV flux varies by nearly an order of magnitude over the past $\sim15$ yr, with multiple transitions between bright and faint states. The source brightened by a factor of $\sim10$ between the 2010 \textit{Chandra} observation and the 2011 \textit{XMM-Newton} high state, then declined by $\sim6$--7 to a low state in 2019, followed by renewed brightening in recent \textit{Swift} monitoring. The X-ray spectrum shows strong state-dependent evolution, changing from a steep high-state continuum ($Γ=2.32\pm0.04$) to a much harder low-state spectrum ($Γ=1.39\pm0.06$). The low-state spectrum also exhibits a narrow Fe K$α$ line with an equivalent width of $\sim330$ eV. Reflection modeling indicates that the low-flux state is strongly reflection dominated, with the reflection fraction increasing from $R_{\rm refl}\sim4$ to $\sim34$, consistent with a compact corona subject to strong light-bending effects. The ultraviolet emission broadly follows the long-term X-ray variability but with smaller amplitude, while the optical and mid-infrared bands vary more mildly. Despite the dramatic X-ray variability, Mrk~382 does not enter an extreme X-ray-weak state, and we did not detect clear optical spectral-type changes based on the currently available observations. Mrk~382 is therefore a rare nearby Seyfert galaxy undergoing recurrent X-ray state transitions, providing a valuable laboratory for studying changing coronal geometry and multiwavelength AGN variability.

astro-ph.HE↗

arXiv2026

A Radio Changing-state Jet in the Narrow-line Seyfert 1 Galaxy J1105+1452

We report the discovery of a radio-quiet to radio-loud transition in the narrow-line Seyfert 1 galaxy J1105+1452. The source has undergone a long-term evolution from a radio-quiet state in the 1990s to a persistently radio-bright state after 2017. Post-2017 flux densities in the $0.8$-$7$ GHz range cluster between $32$ and $43$ mJy, whereas the $144$ MHz flux density is only $1.94 \pm 0.23$ mJy. This indicates strong low-frequency suppression from a compact, absorbed component. Modeling the radio spectral energy distribution with a synchrotron self-absorption model yields a turnover frequency $ν_{\rm p} = 0.48 \pm 0.03$ GHz and a peak flux density $S_{\rm p} = 38.9 \pm 4.7$ mJy. These parameters classify J1105+1452 as a megahertz peaked-spectrum source, consistent with the new episode of an early-stage compact jet. Under the assumption of equipartition, we derive an intrinsic physical radius $R \sim 0.68$ pc and an average apparent expansion velocity $β_{\rm app} \approx 0.64$. The observed brightness temperature $T_b \approx 6.0 \times 10^{11}$ K necessitates a Doppler factor $δ\approx 12$, implying a relativistic jet viewed at $θ\lesssim 5^\circ$. Despite the dramatic radio evolution, the X-ray spectrum remains stable and steep ($Γ\simeq 3.0$), suggesting that the X-ray emission remains dominated by the disk-corona, while the radio band has become jet-dominated. Our results identify J1105+1452 as a rare radio changing-state NLSy1, providing a unique laboratory for studying the birth and early evolution of relativistic jets at high Eddington ratios.

astro-ph.HE↗

arXiv2026

Quantifying Non-linearity in Topology Optimization with similarity based Visualization

Topology optimization (TO) can be viewed as seeking an optimal solution in the design space of a given TO problem. For weakly non-linear TO problems, e.g., compliance minimization, sensitivity-based methods typically converge well, whereas for strongly non-linear problems, e.g., maximum stress minimization, stabilization strategies such as stabilization terms and projection functions are often required to enhance convergence. Especially in scenarios with massive design variables, it is difficult to intuitively demonstrate the non-linear complexity of different TO problems and to elucidate the mechanisms by which stabilization strategies affect convergence. To address this challenge, we propose a visualization framework and a quantitative non-linearity index for objectives with varying complexity. We employ a multi-start fixed-gradient sampling tailored to similarity-based dimensionality reduction while keeping the computational cost under control. The samples are then parameterized via cosine similarity to obtain a low-dimensional visualization surface of the objective function. Based on this visualization, we construct a dimensionless complexity index with a clear geometric interpretation by measuring the gap between the visualization surface and the discrete approximation of its convex envelope, which enables quantitative comparisons of non-linearity across TO tasks, parameter choices, and stabilization strategies. Extensive comparative experiments show that the proposed approach is both adaptable and discriminative on a variety of representative TO problems, and it provides intuitive and measurable guidance for parameter selection.

math.OC↗

arXiv2025

Reveal Hidden Pitfalls and Navigate Next Generation of Vector Similarity Search from Task-Centric Views

Vector Similarity Search (VSS) in high-dimensional spaces is rapidly emerging as core functionality in next-generation database systems for numerous data-intensive services -- from embedding lookups in large language models (LLMs), to semantic information retrieval and recommendation engines. Current benchmarks, however, evaluate VSS primarily on the recall-latency trade-off against a ground truth defined solely by distance metrics, neglecting how retrieval quality ultimately impacts downstream tasks. This disconnect can mislead both academic research and industrial practice. We present Iceberg, a holistic benchmark suite for end-to-end evaluation of VSS methods in realistic application contexts. From a task-centric view, Iceberg uncovers the Information Loss Funnel, which identifies three principal sources of end-to-end performance degradation: (1) Embedding Loss during feature extraction; (2) Metric Misuse, where distances poorly reflect task relevance; (3) Data Distribution Sensitivity, highlighting index robustness across skews and modalities. For a more comprehensive assessment, Iceberg spans eight diverse datasets across key domains such as image classification, face recognition, text retrieval, and recommendation systems. Each dataset, ranging from 1M to 100M vectors, includes rich, task-specific labels and evaluation metrics, enabling assessment of retrieval algorithms within the full application pipeline rather than in isolation. Iceberg benchmarks 13 state-of-the-art VSS methods and re-ranks them based on application-level metrics, revealing substantial deviations from traditional rankings derived purely from recall-latency evaluations. Building on these insights, we define a set of task-centric meta-features and derive an interpretable decision tree to guide practitioners in selecting and tuning VSS methods for their specific workloads.

cs.IR↗

arXiv2025

Long-term Mid-infrared Color Variations of Narrow-Line Seyfert 1 Galaxies

We present a systematic investigation of long-term mid-infrared (MIR) color variability in 1,718 Narrow-Line Seyfert 1 galaxies (NLSy1s) using 14-year \textit{WISE}/NEOWISE monitoring data. Through Pearson correlation analysis between photometric magnitude and color, we identify: (1) a radio-quiet NLSy1 (RQ-NLSy1) population comprising 230 bluer-when-brighter (BWB) sources, 131 redder-when-brighter (RWB) sources, and 1,323 objects showing weak or statistically insignificant color variations; and (2) a radio-loud NLSy1 (RL-NLSy1) population containing 5 BWBs, 2 RWBs, and 27 sources with weak/no color variations. Our analysis reveals that the BWB tendency strengthens significantly in galaxies with redder mean MIR colors $\rm \left $ and lower starlight contamination. Furthermore, this color-change pattern demonstrates that the most bolometric luminous sources exhibit the most pronounced BWB behavior. While similar trends exist for black hole mass and Eddington ratio, bolometric luminosity appears to be the primary physical driver. Potential origins of these variations (e.g., host galaxy contribution, accretion disk variability, and dust reprocessing) are discussed. We conclude that temperature-dependent dust reprocessing dominates the observed BWB, RWB, and no/weak variation patterns. This interpretation may also apply to similar MIR color variations observed in other extragalactic MIR transients, such as tidal disruption events, ambiguous nuclear transients, and changing-look AGNs. In addition, we find no significant difference in long-term MIR color variations between RL-NLSy1s and RQ-NLSy1s, however, RL-NLSy1s show significantly greater dispersion in intrinsic variability amplitude compared to RQ-NLSy1s due to jet-induced complexity, where non-thermal synchrotron emission from relativistic jets obscures thermal dust signatures.

astro-ph.GA↗

arXiv2025

OnePiece: Bringing Context Engineering and Reasoning to Industrial Cascade Ranking System

Despite the growing interest in replicating the scaled success of large language models (LLMs) in industrial search and recommender systems, most existing industrial efforts remain limited to transplanting Transformer architectures, which bring only incremental improvements over strong Deep Learning Recommendation Models (DLRMs). From a first principle perspective, the breakthroughs of LLMs stem not only from their architectures but also from two complementary mechanisms: context engineering, which enriches raw input queries with contextual cues to better elicit model capabilities, and multi-step reasoning, which iteratively refines model outputs through intermediate reasoning paths. However, these two mechanisms and their potential to unlock substantial improvements remain largely underexplored in industrial ranking systems. In this paper, we propose OnePiece, a unified framework that seamlessly integrates LLM-style context engineering and reasoning into both retrieval and ranking models of industrial cascaded pipelines. OnePiece is built on a pure Transformer backbone and further introduces three key innovations: (1) structured context engineering, which augments interaction history with preference and scenario signals and unifies them into a structured tokenized input sequence for both retrieval and ranking; (2) block-wise latent reasoning, which equips the model with multi-step refinement of representations and scales reasoning bandwidth via block size; (3) progressive multi-task training, which leverages user feedback chains to effectively supervise reasoning steps during training. OnePiece has been deployed in the main personalized search scenario of Shopee and achieves consistent online gains across different key business metrics, including over $+2\%$ GMV/UU and a $+2.90\%$ increase in advertising revenue.

cs.IR↗

arXiv2025

AGNs in the extremely overdense galaxy region BOSS 1441: A Chandra observation

We present a Chandra/ACIS-I study of X-ray sources in BOSS 1441, a protocluster at $z=2.32\pm0.02$ that exhibits a prominent overdensity of Ly$α$ emitters (LAEs). Using a 45 ks observation, we identify seven X-ray sources spatially coincident with LAE density peaks. The average X-ray photon index for the seven sources, derived from an absorbed power-law model with Galactic absorption fixed, is 1.49 (ranging from -0.68 to 2.51), corresponding to an average luminosity of $\rm 6.85\times 10^{44}~erg~s^{-1}$ in the rest-frame 2-33 keV band, with individual luminosities spanning $(3.57 - 13.96)\rm\times 10^{44}~erg~s^{-1}$. Three sources exhibit relatively flat spectral slopes. Two are associated with the MAMMOTH-1 nebula, while the third, located at the edge of BOSS 1441 with a $> 5'$ offset from the LAE density peak, resides in a region with a high submillimeter-band density. We estimate the fraction of X-ray detected AGNs among the LAEs to be $11.5^{+3.8}_{-4.6}\%$, approximately double that of previously studied LAEs. This elevated fraction suggests BOSS 1441 is in a mature evolutionary stage, with even higher AGN fractions expected in massive LAEs such as PKS 1138-262. In contrast, the submillimeter galaxy population shows a lower AGN fraction ($6.9^{+6.9}_{-4.5}\%$), consistent with their typically obscured nature. These results indicate that the protocluster's massive galaxies are evolving into the bright red sequence galaxies observed in local clusters, where AGNs likely play a critical role in quenching their star formation.

astro-ph.GA↗

arXiv2025

The Role of Prescreening in Auctions with Predictions

Sellers often prescreen potential bidders, restricting participation to a select group of capable participants. Recent advances in machine learning and generative AI make this strategy increasingly viable by enabling the cost-effective identification of high-quality bidders. However, the practice departs from classic auction theory, which usually favors broad competition over selective exclusion. In this paper, we examine whether and under what conditions bidder prescreening can be justified. We analyze a setting in which bidders have independent and identically distributed private valuations, and the seller observes noisy signals generated by a valuation predictor. The seller determines how many top bidders to admit and, after receiving signals, selects exactly that many with the highest signal-based rankings. We demonstrate that an auction with prescreening is equivalent to a standard auction (i.e., without prescreening) but with correlated valuations. Our analysis shows that, although admitting fewer bidders leads to revenue losses in both second-price and first-price auctions, a more accurate predictor can mitigate or even fully offset these losses. In contrast, prescreening can significantly boost revenue in all-pay auctions; notably, when the predictor is perfect, admitting only two bidders is optimal. All results remain valid in the presence of reserve prices.

cs.GT↗

arXiv2024

Residual Multi-Task Learner for Applied Ranking

Modern e-commerce platforms rely heavily on modeling diverse user feedback to provide personalized services. Consequently, multi-task learning has become an integral part of their ranking systems. However, existing multi-task learning methods encounter two main challenges: some lack explicit modeling of task relationships, resulting in inferior performance, while others have limited applicability due to being computationally intensive, having scalability issues, or relying on strong assumptions. To address these limitations and better fit our real-world scenario, pre-rank in Shopee Search, we introduce in this paper ResFlow, a lightweight multi-task learning framework that enables efficient cross-task information sharing via residual connections between corresponding layers of task networks. Extensive experiments on datasets from various scenarios and modalities demonstrate its superior performance and adaptability over state-of-the-art methods. The online A/B tests in Shopee Search showcase its practical value in large-scale industrial applications, evidenced by a 1.29% increase in OPU (order-per-user) without additional system latency. ResFlow is now fully deployed in the pre-rank module of Shopee Search. To facilitate efficient online deployment, we propose a novel offline metric Weighted Recall@K, which aligns well with our online metric OPU, addressing the longstanding online-offline metric misalignment issue. Besides, we propose to fuse scores from the multiple tasks additively when ranking items, which outperforms traditional multiplicative fusion. The code is released at https://github.com/BrunoTruthAlliance/ResFlow

cs.IR↗