arXiv subjects

Zhenzhen Li

Publications and source records attributed to Zhenzhen Li.

At least 19 recordsLinked to original sources

arXiv2026

Diffusion contrast induced dynamics in a time-periodic competition-diffusion system with equal total resources

This paper investigates a time-periodic, spatiotemporally heterogeneous competition-diffusion system, where two species have different intrinsic growth rates but equal total resources per period. Using asymptotic analysis and principal eigenvalue theory, we systematically examine its dynamics. When both diffusion rates are small, spatial heterogeneity of the difference in time-averaged resources leads to uniform persistence, stable coexistence, and asymptotic spatial segregation; whereas its spatial uniformity produces multiple regimes--fast-diffuser selection, parameter-dependent coexistence, or slow-diffuser dominance--separated by smooth threshold curves under different structural conditions. In the regime where at least one diffusion rate is large, we obtain a detailed classification of the local dynamics, with sharp delineation of parameter regions for stable coexistence, competitive exclusion, and a novel bistable scenario, where both semi-trivial periodic solutions are linearly stable while an unstable coexistence state also exists. We further characterize the asymptotic profiles of positive solutions in the mixed-scale diffusion limit. Our results show that the interplay between temporal periodicity and spatial heterogeneity can overturn the classical "slower diffuser always prevails" principle, leading to a substantially richer range of ecological outcomes.

math.AP↗

arXiv2026

Reason Before You Retrieve: Agentic Planning for Multi-modal RAG

Multimodal retrieval-augmented generation (mRAG) aims to answer image-text queries with external knowledge, but most existing systems still retrieve directly from raw multimodal input over a flat evidence space. This design often struggles with two key challenges: the retrieval target is under-specified because the question intent must be grounded to the correct visual referent, and the search space is weakly structured, forcing semantically distinct evidence to compete in a single global ranking step. We propose MM-R2, a multimodal agentic retrieval framework that reasons before retrieval by explicitly modeling both what to retrieve and where to search. MM-R2 first constructs an intent-grounded retrieval state from the image-question pair, capturing the information need, grounded referent, and retrieval constraints. It then performs retrieval over a structured KnowledgeMap, where the agent selects relevant retrieval units before issuing grounded queries within them. To enable this capability, we build MM-R2-Traj, a large-scale trajectory dataset of multi-step retrieval processes, and adopt a two-stage post-training strategy with supervised fine-tuning and GRPO. Experiments on Infoseek and Encyclopedic VQA datasets show that MM-R2 substantially outperforms strong baselines on answer accuracy while also yielding more interpretable and verifiable retrieval trajectories.

cs.AI↗

arXiv2026

The Narrow-Line Seyfert 1 Phenomenon: Accretion State Versus Host Galaxy Properties

The physical origin of the narrow-line Seyfert 1 (NLS1) and broad-line Seyfert 1 (BLS1) dichotomy remains debated, with competing scenarios invoking host-galaxy evolution or intrinsic accretion physics. We analysed host-galaxy properties and AGN luminosities obtained from CIGALE spectral energy distribution fitting for $\sim$12,000 Type 1 AGNs from the Sloan Digital Sky Survey, of which 29\% are NLS1s. Globally, NLS1s have lower virial black hole masses, higher inferred Eddington ratios, lower stellar masses, and higher specific star formation rates than BLS1s. In the FWHM(\Hb)--$L_{\rm AGN}$ plane, the conventional 2000 km s$^{-1}$ boundary is better viewed as an empirical division within a continuous parameter space rather than a physical threshold, with Fe~II tracing the high-accretion end. In a host-matched subsample of 767 NLS1--BLS1 pairs with statistically indistinguishable stellar mass, black hole mass, and redshift, NLS1s still show higher Eddington ratios, stronger Fe~II emission, and bluer optical continua, together with elevated SFR and dust attenuation, suggesting that the NLS1 phenomenon is most naturally associated with a high-accretion state within the continuous distribution of Type 1 AGNs, while host-galaxy gas supply may also play a role in modulating its strength. In this picture, NLS1 and BLS1 classifications reflect different locations within a continuous accretion sequence of the same underlying population rather than two physically disjoint classes.

astro-ph.GA↗

arXiv2025

Time-series Forecast for Indoor Zone Air Temperature with Long Horizons: A Case Study with Sensor-based Data from a Smart Building

With the press of global climate change, extreme weather and sudden weather changes are becoming increasingly common. To maintain a comfortable indoor environment and minimize the contribution of the building to climate change as much as possible, higher requirements are placed on the operation and control of HVAC systems, e.g., more energy-efficient and flexible to response to the rapid change of weather. This places demands on the rapid modeling and prediction of zone air temperatures of buildings. Compared to the traditional simulation-based approach such as EnergyPlus and DOE2, a hybrid approach combined physics and data-driven is more suitable. Recently, the availability of high-quality datasets and algorithmic breakthroughs have driven a considerable amount of work in this field. However, in the niche of short- and long-term predictions, there are still some gaps in existing research. This paper aims to develop a time series forecast model to predict the zone air temperature in a building located in America on a 2-week horizon. The findings could be further improved to support intelligent control and operation of HVAC systems (i.e. demand flexibility) and could also be used as hybrid building energy modeling.

cs.LG↗

arXiv2025

Catch Me If You Can? Not Yet: LLMs Still Struggle to Imitate the Implicit Writing Styles of Everyday Authors

As large language models (LLMs) become increasingly integrated into personal writing tools, a critical question arises: can LLMs faithfully imitate an individual's writing style from just a few examples? Personal style is often subtle and implicit, making it difficult to specify through prompts yet essential for user-aligned generation. This work presents a comprehensive evaluation of state-of-the-art LLMs' ability to mimic personal writing styles via in-context learning from a small number of user-authored samples. We introduce an ensemble of complementary metrics-including authorship attribution, authorship verification, style matching, and AI detection-to robustly assess style imitation. Our evaluation spans over 40000 generations per model across domains such as news, email, forums, and blogs, covering writing samples from more than 400 real-world authors. Results show that while LLMs can approximate user styles in structured formats like news and email, they struggle with nuanced, informal writing in blogs and forums. Further analysis on various prompting strategies such as number of demonstrations reveal key limitations in effective personalization. Our findings highlight a fundamental gap in personalized LLM adaptation and the need for improved techniques to support implicit, style-consistent generation. To aid future research and for reproducibility, we open-source our data and code.

cs.CL↗

arXiv2025

Human2LocoMan: Learning Versatile Quadrupedal Manipulation with Human Pretraining

Quadrupedal robots have demonstrated impressive locomotion capabilities in complex environments, but equipping them with autonomous versatile manipulation skills in a scalable way remains a significant challenge. In this work, we introduce a cross-embodiment imitation learning system for quadrupedal manipulation, leveraging data collected from both humans and LocoMan, a quadruped equipped with multiple manipulation modes. Specifically, we develop a teleoperation and data collection pipeline, which unifies and modularizes the observation and action spaces of the human and the robot. To effectively leverage the collected data, we propose an efficient modularized architecture that supports co-training and pretraining on structured modality-aligned data across different embodiments. Additionally, we construct the first manipulation dataset for the LocoMan robot, covering various household tasks in both unimanual and bimanual modes, supplemented by a corresponding human dataset. We validate our system on six real-world manipulation tasks, where it achieves an average success rate improvement of 41.9% overall and 79.7% under out-of-distribution (OOD) settings compared to the baseline. Pretraining with human data contributes a 38.6% success rate improvement overall and 82.7% under OOD settings, enabling consistently better performance with only half the amount of robot data. Our code, hardware, and data are open-sourced at: https://human2bots.github.io.

cs.RO↗

arXiv2025

FedRPCA: Enhancing Federated LoRA Aggregation Using Robust PCA

LoRA has emerged as one of the most promising fine-tuning techniques, especially for federated learning (FL), since it significantly reduces communication and computation costs at resource-constrained clients. However, data heterogeneity remains a significant challenge for LoRA-based FL, and the conventional aggregation strategy based on FedAvg suffers from slow convergence and suboptimal accuracy. Motivated by recent advances in model merging, particularly Task Arithmetic, we explore the idea of aggregating client LoRA parameters using scaled averaging. We first observe that a naive application of Task Arithmetic is ineffective due to the high cosine similarity between client updates, indicating significant common knowledge in the updates across clients. To address this issue, we propose decomposing client LoRA updates via Robust Principal Component Analysis (Robust-PCA) into a common low-rank component and client-specific sparse components. Our proposed algorithm FedRPCA aggregates the low-rank components through averaging, consolidating common knowledge, and applies scaled averaging to the sparse components to amplify client-specific knowledge. We evaluate our approach across a variety of vision and language tasks and demonstrate that it achieves higher final accuracy and faster convergence compared to competing baselines.

cs.LG↗

arXiv2025

Extended Emission-line Region in a Poststarburst Galaxy Hosting Tidal Disruption Event AT2019qiz and Quasiperiodic Eruptions

We present a comprehensive analysis of the extended emission line region (EELR) in the host galaxy of the tidal disruption event (TDE) AT2019qiz, utilizing VLT/MUSE integral-field spectroscopy. The high spatial-resolution data reveal a bi-conical emission structure approximately $3.7~\mathrm{kpc}$ in scale within the galactic center, characterized by a prominent [OIII] line in the nucleus and significant [NII] line emission extending into the EELR. Spectral analysis of the EELR indicates line ratios consistent with Seyfert ionization in the center and LINER-type ionization in the outer diffuse region, suggesting ionization from galactic nuclear activity. The required ionizing luminosity, estimated from the H$\rm{\alpha}$ and H$\rm{\beta}$ luminosities based on the photoionization and recombination balance assumption, is $10^{41.8}$ $\mathrm{erg\,s^{-1}}$ for all spaxels classified as active galactic nucleus (AGN), and $10^{40.7}$ $\mathrm{erg\,s^{-1}}$ for spaxels in the central $0.9~\mathrm{kpc}$ Seyfert region. However, the current bolometric luminosity of the nucleus $L_{\text{bol}} \leq 10^{40.8}\,\mathrm{erg\,s^{-1}}$, estimated from quiescent-state soft X-ray observations, is insufficient to ionize the entire EELR, implying a recently faded AGN or a delayed response to historical activity. Stellar population analysis reveals a post-starburst characteristic in the EELR, and the gas kinematics show disturbances and non-circular components compared to the stellar kinematics. Notably, the recent detection of quasi-periodic eruptions (QPEs) in the X-ray light curve of AT2019qiz confirms the TDE-QPE association. Our findings provide direct evidence for an AGN-like EELR in the host galaxy of the nearest TDE with QPE detection, offering new insights into the complex interplay between TDEs, QPEs, AGN activity, and host galaxy evolution.

astro-ph.GA↗

arXiv2024

Serendipitous Catch of a Giant Jellyfish: an Ionized Nebula around 3C 275.1 with 170 kpc Long Tails

3C 275.1 is a blue quasar at $z=0.55522$, hosting powerful outflows and residing in a complex environment. We present a serendipitously detected giant nebula surrounding 3C 275.1, which shows morphological features resembling those of objects known as "jellyfish galaxy", with extremely long tails of ionized gas extending to 170 kpc in projection. We analyze its optical spectra taken by the MUSE on the VLT. The brighter part of this giant nebula exceeds 100 kpc, whose rotation curve does not flatten out, is very different from those of normal spiral galaxies. This system shares some characteristics common to those formed via ram pressure stripping (RPS), yet its long narrow tails and higher ionization are unusual compared to known tails in jellyfish galaxies, not fully consistent with a simple RPS scenario. Our photoionization simulation and the inferred short recombination timescale both suggest that besides the quasar 3C 275.1, an extra source of ionization is necessary to keep the gas ionized at such distance from the nucleus, which could be related to RPS, tidal interaction or AGN outflow, providing new evidence of active dynamical interaction of a galaxy with the intracluster medium.

astro-ph.GA↗

arXiv2024

Auto-Train-Once: Controller Network Guided Automatic Network Pruning from Scratch

Current techniques for deep neural network (DNN) pruning often involve intricate multi-step processes that require domain-specific expertise, making their widespread adoption challenging. To address the limitation, the Only-Train-Once (OTO) and OTOv2 are proposed to eliminate the need for additional fine-tuning steps by directly training and compressing a general DNN from scratch. Nevertheless, the static design of optimizers (in OTO) can lead to convergence issues of local optima. In this paper, we proposed the Auto-Train-Once (ATO), an innovative network pruning algorithm designed to automatically reduce the computational and storage costs of DNNs. During the model training phase, our approach not only trains the target model but also leverages a controller network as an architecture generator to guide the learning of target model weights. Furthermore, we developed a novel stochastic gradient algorithm that enhances the coordination between model training and controller network training, thereby improving pruning performance. We provide a comprehensive convergence analysis as well as extensive experiments, and the results show that our approach achieves state-of-the-art performance across various model architectures (including ResNet18, ResNet34, ResNet50, ResNet56, and MobileNetv2) on standard benchmark datasets (CIFAR-10, CIFAR-100, and ImageNet).

cs.CV↗

arXiv2023

Leveraging Foundation Models to Improve Lightweight Clients in Federated Learning

Federated Learning (FL) is a distributed training paradigm that enables clients scattered across the world to cooperatively learn a global model without divulging confidential data. However, FL faces a significant challenge in the form of heterogeneous data distributions among clients, which leads to a reduction in performance and robustness. A recent approach to mitigating the impact of heterogeneous data distributions is through the use of foundation models, which offer better performance at the cost of larger computational overheads and slower inference speeds. We introduce foundation model distillation to assist in the federated training of lightweight client models and increase their performance under heterogeneous data settings while keeping inference costs low. Our results show improvement in the global model performance on a balanced testing set, which contains rarely observed samples, even under extreme non-IID client data distributions. We conduct a thorough evaluation of our framework with different foundation model backbones on CIFAR10, with varying degrees of heterogeneous data distributions ranging from class-specific data partitions across clients to dirichlet data sampling, parameterized by values between 0.01 and 1.0.

cs.LG↗

arXiv2023

Text-driven Prompt Generation for Vision-Language Models in Federated Learning

Prompt learning for vision-language models, e.g., CoOp, has shown great success in adapting CLIP to different downstream tasks, making it a promising solution for federated learning due to computational reasons. Existing prompt learning techniques replace hand-crafted text prompts with learned vectors that offer improvements on seen classes, but struggle to generalize to unseen classes. Our work addresses this challenge by proposing Federated Text-driven Prompt Generation (FedTPG), which learns a unified prompt generation network across multiple remote clients in a scalable manner. The prompt generation network is conditioned on task-related text input, thus is context-aware, making it suitable to generalize for both seen and unseen classes. Our comprehensive empirical evaluations on nine diverse image classification datasets show that our method is superior to existing federated prompt learning methods, that achieve overall better generalization on both seen and unseen classes and is also generalizable to unseen datasets.

cs.CV↗

arXiv2023

Cross-corpus Readability Compatibility Assessment for English Texts

Text readability assessment has gained significant attention from researchers in various domains. However, the lack of exploration into corpus compatibility poses a challenge as different research groups utilize different corpora. In this study, we propose a novel evaluation framework, Cross-corpus text Readability Compatibility Assessment (CRCA), to address this issue. The framework encompasses three key components: (1) Corpus: CEFR, CLEC, CLOTH, NES, OSP, and RACE. Linguistic features, GloVe word vector representations, and their fusion features were extracted. (2) Classification models: Machine learning methods (XGBoost, SVM) and deep learning methods (BiLSTM, Attention-BiLSTM) were employed. (3) Compatibility metrics: RJSD, RRNSS, and NDCG metrics. Our findings revealed: (1) Validated corpus compatibility, with OSP standing out as significantly different from other datasets. (2) An adaptation effect among corpora, feature representations, and classification methods. (3) Consistent outcomes across the three metrics, validating the robustness of the compatibility assessment framework. The outcomes of this study offer valuable insights into corpus selection, feature representation, and classification methods, and it can also serve as a beginning effort for cross-corpus transfer learning.

cs.CL↗

arXiv2022

Long-time behavior of a nonlocal dispersal logistic model with seasonal succession

This paper is devoted to a nonlocal dispersal logistic model with seasonal succession in one-dimensional bounded habitat, where the seasonal succession accounts for the effect of two different seasons. Firstly, we provide the persistence-extinction criterion for the species, which is different from that for local diffusion model. Then we show the asymptotic profile of the time-periodic positive solution as the species persists in long run.

math.AP↗

arXiv2022

Asymptotic Escape of Spurious Critical Points on the Low-rank Matrix Manifold

We show that on the manifold of fixed-rank and symmetric positive semi-definite matrices, the Riemannian gradient descent algorithm almost surely escapes some spurious critical points on the boundary of the manifold. Our result is the first to partially overcome the incompleteness of the low-rank matrix manifold without changing the vanilla Riemannian gradient descent algorithm. The spurious critical points are some rank-deficient matrices that capture only part of the eigen components of the ground truth. Unlike classical strict saddle points, they exhibit very singular behavior. We show that using the dynamical low-rank approximation and a rescaled gradient flow, some of the spurious critical points can be converted to classical strict saddle points in the parameterized domain, which leads to the desired result. Numerical experiments are provided to support our theoretical findings.

math.OC↗

arXiv2022

TTAGN: Temporal Transaction Aggregation Graph Network for Ethereum Phishing Scams Detection

In recent years, phishing scams have become the most serious type of crime involved in Ethereum, the second-largest blockchain platform. The existing phishing scams detection technology on Ethereum mostly uses traditional machine learning or network representation learning to mine the key information from the transaction network to identify phishing addresses. However, these methods adopt the last transaction record or even completely ignore these records, and only manual-designed features are taken for the node representation. In this paper, we propose a Temporal Transaction Aggregation Graph Network (TTAGN) to enhance phishing scams detection performance on Ethereum. Specifically, in the temporal edges representation module, we model the temporal relationship of historical transaction records between nodes to construct the edge representation of the Ethereum transaction network. Moreover, the edge representations around the node are aggregated to fuse topological interactive relationships into its representation, also named as trading features, in the edge2node module. We further combine trading features with common statistical and structural features obtained by graph neural networks to identify phishing addresses. Evaluated on real-world Ethereum phishing scams datasets, our TTAGN (92.8% AUC, and 81.6% F1score) outperforms the state-of-the-art methods, and the effectiveness of temporal edges representation and edge2node module is also demonstrated.

cs.CR↗

arXiv2021

Fast Global Convergence for Low-rank Matrix Recovery via Riemannian Gradient Descent with Random Initialization

In this paper, we propose a new global analysis framework for a class of low-rank matrix recovery problems on the Riemannian manifold. We analyze the global behavior for the Riemannian optimization with random initialization. We use the Riemannian gradient descent algorithm to minimize a least squares loss function, and study the asymptotic behavior as well as the exact convergence rate. We reveal a previously unknown geometric property of the low-rank matrix manifold, which is the existence of spurious critical points for the simple least squares function on the manifold. We show that under some assumptions, the Riemannian gradient descent starting from a random initialization with high probability avoids these spurious critical points and only converges to the ground truth in nearly linear convergence rate, i.e. $\mathcal{O}(\text{log}(\frac{1}ε)+ \text{log}(n))$ iterations to reach an $ε$-accurate solution. We use two applications as examples for our global analysis. The first one is a rank-1 matrix recovery problem. The second one is a generalization of the Gaussian phase retrieval problem. It only satisfies the weak isometry property, but has behavior similar to that of the first one except for an extra saddle set. Our convergence guarantee is nearly optimal and almost dimension-free, which fully explains the numerical observations. The global analysis can be potentially extended to other data problems with random measurement structures and empirical least squares loss functions.

stat.ML↗

arXiv2020

Meta-Learning for Neural Relation Classification with Distant Supervision

Distant supervision provides a means to create a large number of weakly labeled data at low cost for relation classification. However, the resulting labeled instances are very noisy, containing data with wrong labels. Many approaches have been proposed to select a subset of reliable instances for neural model training, but they still suffer from noisy labeling problem or underutilization of the weakly-labeled data. To better select more reliable training instances, we introduce a small amount of manually labeled data as reference to guide the selection process. In this paper, we propose a meta-learning based approach, which learns to reweight noisy training data under the guidance of reference data. As the clean reference data is usually very small, we propose to augment it by dynamically distilling the most reliable elite instances from the noisy data. Experiments on several datasets demonstrate that the reference data can effectively guide the selection of training data, and our augmented approach consistently improves the performance of relation classification comparing to the existing state-of-the-art methods.

cs.CL↗