arXiv subjects

Xiang Ji

Publications and source records attributed to Xiang Ji.

At least 37 records · Page 2Linked to original sources

arXiv2025

MCI: Multi-Channel Imager on the Chinese Space Station Survey Telescope

The Multi-Channel Imager (MCI) is a powerful near-ultraviolet (NUV) and visible imager onboard the Chinese Space Station Survey Telescope (CSST). The MCI provides three imaging channels, which are the NUV channel, the Blue channel and the Red channel, with the wavelength range of 255-430 nm, 430-700 nm, and 700-1000 nm, respectively. MCI's three channels can target the same field simultaneously, which is unique compared to other imagers onboard the Hubble Space Telescope (HST) or the James Webb Space Telescope (JWST). Each channel employs a CCD focal plane of 9216 x 9232 pixels and $\sim$7\arcmin.5 x 7\arcmin.5 field of view (FOV), which are about $\gtrsim 4$ times greater than the FOVs of HST imagers. The MCI's three channels feature unprecedented sensitivities and field of views complement the NUV and visible capabilities of the CSST for high-precision photometry and weak-signal detection, which would help build a new standard-star system and the deepest UV-Optical exposures for CSST. Rich filter sets of MCI would help explore other sciences such as local emission line mapping, high-z Ly$α$ emitters searching, etc. Here we present key design features, results of current ground tests, and suggested observing strategies of the MCI.

astro-ph.IM↗

arXiv2025

A Large-Scale Multimodal Dataset and Benchmarks for Human Activity Scene Understanding and Reasoning

Multimodal human action recognition (HAR) leverages complementary sensors for activity classification. Beyond recognition, recent advances in large language models (LLMs) enable detailed descriptions and causal reasoning, motivating new tasks: human action understanding (HAU) and human action reasoning (HARn). However, most LLMs, especially large vision language models (LVLMs), struggle with non-RGB modalities such as depth, IMU, and mmWave due to the lack of large-scale data-caption resources. Existing HAR datasets mainly provide coarse data-label annotations, which are insufficient to capture fine-grained action dynamics needed for HAU and HARn. We consider two ground-truth pair types: (1) data label (discrete category) and (2) data caption (textual description). Naively generating captions from labels often lacks logical and spatiotemporal consistency. We introduce CUHK-X, a large-scale multimodal dataset and benchmark suite for HAR, HAU, and HARn. CUHK-X contains 58,445 samples covering 40 actions performed by 30 participants across two indoor environments. To improve caption consistency, we propose a prompt-based scene creation method that leverages LLMs to generate logically connected activity sequences, followed by human validation. CUHK-X includes three benchmarks with six evaluation tasks. Experiments report average accuracies of 76.52% (HAR), 40.76% (HAU), and 70.25% (HARn). CUHK-X aims to enable the community to apply and develop data-intensive learning methods for robust, multimodal human activity analysis. Project page and code: https://openaiotlab.github.io/CUHK-X/ and https://github.com/openaiotlab/CUHK-X.

cs.CV↗

arXiv2025

Building a Radio AGN Sample from Cosmic Morning -- The Radio High-Redshift Quasar Catalog (RHzQCat): I. Catalog from SDSS Quasars and Radio Surveys at $z > 3$

Radio-loud high-redshift quasars (RHRQs) provide crucial insights into the evolution of relativistic jets and their connection to the growth of supermassive black holes. Beyond the extensively studied population at $z \ge 5$, the cosmic morning epoch ($3 \lesssim z \lesssim 5$) marks the peak of active galactic nucleus (AGN) activity and black hole accretion, yet remains relatively unexplored. In this work, we compiled the radio high-redshift quasar catalog (RHzQCat) by cross-matching the SDSS DR16Q catalog with four major radio surveys -- FIRST,NVSS, RACS, and GLEAM. Our tier-based cross-matching framework and visual validation ensured reliable source identification across surveys with diverse beam sizes. The catalog included 1629 reliable and 315 candidate RHRQs, with radio luminosities uniformly spanning $10^{25.5}$ -- $10^{29.3}$ W Hz$^{-1}$. About 95\% of the confirmed sources exhibited compact morphologies, consistent with Doppler-boosted or young AGN populations at high redshifts. Our catalog increases the number of known RHRQs at $z\ge3$ by an order of magnitude, representing the largest and most homogeneous catalog of radio quasars at cosmic morning, filling the observational gap between the early ($z>6$) and local Universe. It provides a robust reference for future statistical studies of jet evolution, AGN feedback, and cosmic magnetism with next-generation facilities such as the Square Kilometer Array (SKA).

astro-ph.GA↗

arXiv2025

Maximization of Supercapacitor Storage via Topology Optimization of Electrode Structures

As widely used electrochemical storage devices, supercapacitors deliver higher power density than batteries, but suffer from significantly lower energy density. In this work, we propose a topology optimization model for electrode structure to maximize energy storage in supercapacitors. The existence of minimizers to the resulting optimal control problem, which is constrained by a modified steady-state Poisson--Nernst--Planck system describing ionic electrodiffusion, has been theoretically established by using the direct method in the calculus of variation. Sensitivity analysis of the topology optimization model is performed to derive variational derivatives and corresponding adjoint equations. A gradient flow formulation discretized by a stabilized semi-implicit scheme is developed to solve the resulting topology optimization problem. Extensive numerical experiments present various porous electrode structures that own large area of electrode-electrolyte interface, demonstrating the effectiveness and robustness of the proposed topology optimization model and corresponding algorithm.

math.OC↗

arXiv2025

A pilot VLBI study of the SQUAB quasar sample featuring multiple Gaia detections

Our previous work identified a class of SDSS quasars exhibiting multiple Gaia detections, classifying them as candidates for various astrophysical systems such as quasar-star pairs, dual quasars, and gravitationally lensed quasars. In this paper, we present a pilot VLBI study targeting a radio-bright subsample and report the first high-resolution imaging results. By leveraging the milliarcsecond-scale resolution of VLBI and its precise astrometric coordination incorporating with Gaia, we aim to refine the classification of these multiple matched sources, search for potential dual AGNs, and assess the efficacy of the combined Gaia-VLBI approach in resolving ambiguous quasar systems. We cross-matched the SQUAB quasar sample with the FIRST and NVSS catalogs, identifying 18 radio-emitting sources. The three brightest were selected for dual-frequency (1.6 and 4.9 GHz) VLBA observations. We performed VLBI imaging at both Gaia positions, constructed spectral index maps, and estimated brightness temperatures to characterize the radio morphology and physical properties. For the three target sources, our VLBI observations reveal compact radio structures consistent with single AGN at the primary Gaia positions. No significant emission is detected at the secondary Gaia locations. These results support the interpretation of the sources as quasar$-$star pairs, in line with earlier studies. This pilot study demonstrates the value of radio-VLBI high-resolution follow-ups on Gaia-selected quasar systems with multiple counterparts, showing how they can unambiguously reveal the true nature of these systems and help remove contaminants from dual AGN candidate samples.

astro-ph.HE↗

arXiv2025

Joint Analysis of Optical, Near-Infrared And Mid-Infrared Variability of 4 Quasars at Redshift < 1

Amid rapid advances in time-domain astronomy, multi-wavelength (e.g., optical and infrared) time-domain studies of quasars remain scarce. Here we present a systematic analysis of four quasars initially selected by their Ks-band variability amplitudes in the VISTA Variables in the V\'ıa Láctea Survey (VVV/VVVX). For these objects, we obtain complementary optical light curves from Pan-STARRS1 (PS1) and the Zwicky Transient Facility (ZTF), and W1-band light curves from the Wide-field Infrared Survey Explorer (WISE). We perform correlation analysis to study the time lags between different bands, which may be directly related to the size of the dust torus. After correcting for infrared flux contamination from the accretion disk and accounting for the redshift effect, we measure the Ks-optical and W1-optical lags for the targets VVV J1834-2925 and VVV J1845-2426. Using typical sublimation temperatures and reverberation time lags, we obtain a graphite-to-silicate grain size ratio of $\frac{a_C}{a_S}\sim$ 0.4. Through SED fitting, we determine the luminosities of these quasars and find that their dust torus sizes follow the established $R_{dust}-L_{AGN}$ relation reported in previous studies.

astro-ph.GA↗

arXiv2025

Optimizing Long-term Variability of AGN Light Curves. I. A Case Study with ZTF Observations in the EGS Field

Optical variability is a key observational probe for studying the accretion dynamics and central engine physics of Active Galactic Nuclei (AGNs). The quality and completeness of light curves have a direct impact on variability studies, particularly for faint AGNs and high-redshift AGNs. To improve the quality of long-term light curves for AGNs, we bin and stack multi-epoch images balancing the image depths and temporal resolution. As a case study, we apply this method to Zwicky Transient Facility (ZTF) observations in the Extended Groth Strip (EGS) field, where the overlapping region covers an area of about 370 arcmin$^2$ and includes $g$-band and $r$-band data taken from March 2018 to December 2024. The co-added images are approximately 2.0 to 2.5 magnitudes deeper than the ZTF single-epoch images. With co-added images, we construct light curves for 73 AGNs in the EGS field. Compared to the traditional ZTF light curves, our light curves maintain consistent long-term variability trends but with higher photometric precision. Furthermore, this method can help detect AGNs with weak variability which are missed from the traditional ZTF data due to the noisy light curves or below the detection limit in ZTF's single-epoch exposure. Among the 73 AGNs, the majority exhibit a bluer-when-brighter (BWB) trend on long-term timescales, which is consistent with previous studies. This work offers insights for optimizing AGN light curves in both current and upcoming all-sky time-domain surveys.

astro-ph.GA↗

arXiv2025

All-in-One Transferring Image Compression from Human Perception to Multi-Machine Perception

Efficiently transferring Learned Image Compression (LIC) model from human perception to machine perception is an emerging challenge in vision-centric representation learning. Existing approaches typically adapt LIC to downstream tasks in a single-task manner, which is inefficient, lacks task interaction, and results in multiple task-specific bitstreams. In this paper, we propose a multi-task adaptation framework that enables transferring a pre-trained base codec to multiple machine vision tasks through a unified model and a single training process. To achieve this, we design an asymmetric adaptation architecture consisting of a task-agnostic encoder adaptation and task-specific decoder adaptation. Furthermore, we introduce two feature propagation modules to facilitate inter-task and inter-scale feature represenation learning. Experiments on PASCAL-Context and NYUD-V2 dataset demonstrate that our method outperforms both Fully Fine-Tuned and other Parameter Efficient Fine-Tuned (PEFT) baselines. Code will be released.

cs.CV↗

arXiv2025

LLM-based Text Simplification and its Effect on User Comprehension and Cognitive Load

Information on the web, such as scientific publications and Wikipedia, often surpasses users' reading level. To help address this, we used a self-refinement approach to develop a LLM capability for minimally lossy text simplification. To validate our approach, we conducted a randomized study involving 4563 participants and 31 texts spanning 6 broad subject areas: PubMed (biomedical scientific articles), biology, law, finance, literature/philosophy, and aerospace/computer science. Participants were randomized to viewing original or simplified texts in a subject area, and answered multiple-choice questions (MCQs) that tested their comprehension of the text. The participants were also asked to provide qualitative feedback such as task difficulty. Our results indicate that participants who read the simplified text answered more MCQs correctly than their counterparts who read the original text (3.9% absolute increase, p<0.05). This gain was most striking with PubMed (14.6%), while more moderate gains were observed for finance (5.5%), aerospace/computer science (3.8%) domains, and legal (3.5%). Notably, the results were robust to whether participants could refer back to the text while answering MCQs. The absolute accuracy decreased by up to ~9% for both original and simplified setups where participants could not refer back to the text, but the ~4% overall improvement persisted. Finally, participants' self-reported perceived ease based on a simplified NASA Task Load Index was greater for those who read the simplified text (absolute change on a 5-point scale 0.33, p<0.05). This randomized study, involving an order of magnitude more participants than prior works, demonstrates the potential of LLMs to make complex information easier to understand. Our work aims to enable a broader audience to better learn and make use of expert knowledge available on the web, improving information accessibility.

cs.CL↗

arXiv2025

Tree-NeRV: A Tree-Structured Neural Representation for Efficient Non-Uniform Video Encoding

Implicit Neural Representations for Videos (NeRV) have emerged as a powerful paradigm for video representation, enabling direct mappings from frame indices to video frames. However, existing NeRV-based methods do not fully exploit temporal redundancy, as they rely on uniform sampling along the temporal axis, leading to suboptimal rate-distortion (RD) performance. To address this limitation, we propose Tree-NeRV, a novel tree-structured feature representation for efficient and adaptive video encoding. Unlike conventional approaches, Tree-NeRV organizes feature representations within a Binary Search Tree (BST), enabling non-uniform sampling along the temporal axis. Additionally, we introduce an optimization-driven sampling strategy, dynamically allocating higher sampling density to regions with greater temporal variation. Extensive experiments demonstrate that Tree-NeRV achieves superior compression efficiency and reconstruction quality, outperforming prior uniform sampling-based methods. Code will be released.

cs.CV↗

arXiv2025

SCOPE-DTI: Semi-Inductive Dataset Construction and Framework Optimization for Practical Usability Enhancement in Deep Learning-Based Drug Target Interaction Prediction

Deep learning-based drug-target interaction (DTI) prediction methods have demonstrated strong performance; however, real-world applicability remains constrained by limited data diversity and modeling complexity. To address these challenges, we propose SCOPE-DTI, a unified framework combining a large-scale, balanced semi-inductive human DTI dataset with advanced deep learning modeling. Constructed from 13 public repositories, the SCOPE dataset expands data volume by up to 100-fold compared to common benchmarks such as the Human dataset. The SCOPE model integrates three-dimensional protein and compound representations, graph neural networks, and bilinear attention mechanisms to effectively capture cross domain interaction patterns, significantly outperforming state-of-the-art methods across various DTI prediction tasks. Additionally, SCOPE-DTI provides a user-friendly interface and database. We further validate its effectiveness by experimentally identifying anticancer targets of Ginsenoside Rh1. By offering comprehensive data, advanced modeling, and accessible tools, SCOPE-DTI accelerates drug discovery research.

cs.LG↗

arXiv2025

MATH-Perturb: Benchmarking LLMs' Math Reasoning Abilities against Hard Perturbations

Large language models have demonstrated impressive performance on challenging mathematical reasoning tasks, which has triggered the discussion of whether the performance is achieved by true reasoning capability or memorization. To investigate this question, prior work has constructed mathematical benchmarks when questions undergo simple perturbations -- modifications that still preserve the underlying reasoning patterns of the solutions. However, no work has explored hard perturbations, which fundamentally change the nature of the problem so that the original solution steps do not apply. To bridge the gap, we construct MATH-P-Simple and MATH-P-Hard via simple perturbation and hard perturbation, respectively. Each consists of 279 perturbed math problems derived from level-5 (hardest) problems in the MATH dataset (Hendrycksmath et. al., 2021). We observe significant performance drops on MATH-P-Hard across various models, including o1-mini (-16.49%) and gemini-2.0-flash-thinking (-12.9%). We also raise concerns about a novel form of memorization where models blindly apply learned problem-solving skills without assessing their applicability to modified contexts. This issue is amplified when using original problems for in-context learning. We call for research efforts to address this challenge, which is critical for developing more robust and reliable reasoning models.

cs.LG↗

arXiv2025

Improving Vision-Language-Action Model with Online Reinforcement Learning

Recent studies have successfully integrated large vision-language models (VLMs) into low-level robotic control by supervised fine-tuning (SFT) with expert robotic datasets, resulting in what we term vision-language-action (VLA) models. Although the VLA models are powerful, how to improve these large models during interaction with environments remains an open question. In this paper, we explore how to further improve these VLA models via Reinforcement Learning (RL), a commonly used fine-tuning technique for large models. However, we find that directly applying online RL to large VLA models presents significant challenges, including training instability that severely impacts the performance of large models, and computing burdens that exceed the capabilities of most local machines. To address these challenges, we propose iRe-VLA framework, which iterates between Reinforcement Learning and Supervised Learning to effectively improve VLA models, leveraging the exploratory benefits of RL while maintaining the stability of supervised learning. Experiments in two simulated benchmarks and a real-world manipulation suite validate the effectiveness of our method.

cs.RO↗

arXiv2025

Floquet engineering of point-gapped topological superconductors

Non-Hermitian systems exhibit two distinct topological classifications based on their gap structure: line-gap and point-gap topologies. Although point-gap topology is intrinsic to non-Hermitian systems, its systematic construction remains a challenge. Here, we present the Floquet engineering approach for realizing point-gapped topological superconductors. By combining Floquet theory with particle-hole symmetry (PHS), we show that a point gap hosting robust Majorana edge modes emerges at the overlap of Floquet bands with opposite winding numbers. In the thermodynamic limit, even weak non-Hermiticity opens a point gap from a gapless spectrum, driving a topological phase transition and breaking non-Bloch parity-time ($\mathcal{PT}$) symmetry. This transition is accompanied by the appearance of the Floquet $Z_2$ skin effect. Furthermore, the point-gapped topological phase and the non-Bloch $\mathcal{PT}$ symmetry exhibit size-dependent phenomena driven by the critical skin effect. Our work offers a new pathway for exploring the point-gapped topological phases in non-Hermitian systems.

cond-mat.supr-con↗

arXiv2024

Discovery of Local Analogs to JWST's Little Red Dots

Recently, the James Webb Space Telescope (JWST) has revealed a new class of high redshift (high-$z$, $z>4$) compact galaxies which are red in the rest-frame optical and blue in the rest-frame UV as V-shaped spectral energy distributions (SEDs), referred to as "Little Red Dots" (LRDs). It is very likely that LRDs host obscured broad-line active galactic nuclei (AGNs). In the meanwhile, Green pea galaxies (GPs), which are compact dwarf galaxies at low redshift, share various similar properties with high redshift star-forming galaxies. Here we aim to find the connection between the LRDs and GPs hosting broad-line AGNs (BLGPs). With a sample of 19 BLGPs obtained from our previous work, we further identify 7 GPs with V-shaped rest-frame UV-to-optical SEDs that are likely local analogs to LRDs. These V-shaped BLGPs exhibit faint UV absolute magnitudes and sub-Eddington rates similar to those of LRDs. Three of them occupy a similar region as LRDs in the BPT diagram, suggesting they have similar ionization conditions and gas-phase metallicities to LRDs. These similarities suggest that V-shaped BLGPs can be taken as local analogs of high-redshift LRDs. In addition, most (16/19) BLGPs, including 6 V-shaped BLGPs, host over-massive black holes above the local $M_{\rm BH}$-$M_{*}$ relation, making it the first sample of galaxies hosting over-massive black holes at $z<0.4$. These findings will help us learn more about the formation and co-evolution of early galaxies and black holes.

astro-ph.GA↗

arXiv2024

Searching for dual/lensed quasar candidates with spectroscopic surveys

Dual and lensed quasars are valuable astrophysical targets in many aspects. Dual quasars, considered as the precursors of supermassive black hole binaries, can provide crucial insights into how black hole mergers drive the growth of supermassive black holes and influence the evolution of galaxies. Lensed quasars, formed by the gravitational deflection of a background quasar's light by a massive foreground object, can address key cosmological questions, particularly in refining measurements of the Hubble constant. Despite their significance, the number of confirmed dual and lensed quasars remains limited. Here in this work, we propose a systematic search for dual/lensed quasars using broad emission line profile diagnostics. Our parent sample consists of spectroscopic quasars from the Dark Energy Spectroscopic Instrument Early Data Release (DESI EDR) and the SDSS DR17 catalog. We identify 30 lensed quasar candidates with similar broad emission line profiles, as well as 36 dual quasar candidates with different profiles. Cross-matching these 66 targets with the HST archival database, we find four overlapping targets, including three previously reported lensed quasars and one newly identified dual quasar candidate. We estimate the black hole masses for the two cores in the same system. The mass ratios are similar in the lensed quasar scenario but vary widely for dual quasars, consistent with the physical nature of these two types. In particular, we identified a dual quasar candidate with the mass ratio exceeding 100 times. We aim to discover more dual/lensed quasar candidates using our method with the upcoming future spectroscopic surveys.

astro-ph.GA↗

arXiv2024

Intermediate-Mass Black Holes in Green Pea Galaxies (IMBH-GP) I: a Candidate Sample from LAMOST and SDSS

The scaling relation of central massive black holes (MBHs) and their host galaxies is well-studied for supermassive BHs (SMBHs, $M_{\rm BH}\ \ge 10^6\, M_{\rm \odot}$). However, this relation has large uncertainties in the mass range of the intermediate-mass BHs (IMBHs, $M_{\rm BH}\ \sim10^3-10^{6}\, M_{\rm \odot}$). Since Green Pea (GP) galaxies are luminous compact dwarf galaxies, which may be likely to host less massive SMBHs or even IMBHs, we systematically search for MBHs in a large sample of 2190 GP galaxies at $z < 0.4$, selected from LAMOST and SDSS spectroscopic surveys. Here, we report a newly discovered sample of 59 MBH candidates with broad H$α$ lines. This sample has a median stellar mass of $10^{8.83\pm0.11}\, M_{\rm \odot}$ and hosts MBHs with single-epoch virial masses ranging from $M_{\rm BH}\ \sim 10^{4.7}$ to $10^{8.5}\, M_{\rm \odot}$ (median $10^{5.85\pm0.64}\, M_{\rm \odot}$). Among the 59 MBH candidates, 36 have black hole masses $M_{\rm BH} \le 10^{6}\, M_{\rm \odot}$ (IMBH candidates), one of which even has $M_{\rm BH} \ \lesssim 10^{5}\, M_{\rm \odot}$. We find that the $M_{\rm BH}-M_{\rm *}$ relation of our MBH sample is consistent with the $M_{\rm BH}-M_{\rm bulge}$ relation for SMBHs, while is above the $M_{\rm BH}-M_{\rm *}$ relation for MBHs in dwarf galaxies in the same mass range. Furthermore, we show that 25 MBH candidates, including 4 IMBH candidates, have additional evidence of black hole activities, assessed through various methods such as the broad-line width, BPT diagram, mid-infrared color, X-ray luminosity, and radio emission. Our studies show that it is very promising to find IMBHs in GP galaxies, and the BH sample so obtained enables us to probe the connection between the MBHs and compact dwarf galaxies in the low-redshift Universe.

astro-ph.GA↗

arXiv2024

Entropy Increasing Numerical Methods for Prediction of Reversible and Irreversible Heating in Supercapacitors

Accurate characterization of entropy plays a pivotal role in capturing reversible and irreversible heating in supercapacitors during charging/discharging cycles. However, numerical methods that can faithfully capture entropy variation in supercapacitors are still in lack. This work develops first-order and second-order finite-volume schemes for the prediction of non-isothermal electrokinetics in supercapacitors. Semi-implicit discretization that decouples temperature from ionic concentrations and electric potential results in an efficient first-order accurate scheme. Its numerical analysis theoretically establishes the unique solvability of the nonlinear scheme with the existence of positive ionic concentrations and temperature at discrete level. To obtain an entropy-increasing second-order scheme, a modified Crank-Nicolson approach is proposed for discretization of the logarithm of both temperature and ionic concentrations, which is employed to enforce numerical positivity. Moreover, numerical analysis rigorously demonstrates that both first-order and second-order schemes are able to unconditionally preserve ionic mass conservation and original entropy increase for a closed, thermally insulated supercapacitor. Extensive numerical simulations show that the proposed schemes have expected accuracy and robust performance in preserving the desired properties. Temperature oscillation in the charging/discharging processes is successfully predicted, unraveling a quadratic scaling law of temperature rising slope against voltage scanning rate. It is also demonstrated that the variation of ionic entropy contribution, which is the underlying mechanism responsible for reversible heating, is faithfully captured. Our work provides a promising tool in predicting reversible and irreversible heating in supercapacitors.

math.NA↗