arXiv subjects

Jing Li

Publications and source records attributed to Jing Li.

At least 307 records · Page 17Linked to original sources

arXiv2025

A 0.3\% calibration of the W UMa-type contact binary luminosity based on Gaia DR3

W Ursa Majoris (W UMa)-type contact binary systems (CBs) with period--luminosity (PL) relations are valuable distance indicators. The PL relations of CBs are affected by metallicity. Here, we establish PL relations and period--luminosity--metallicity (PLZ) relations in nine bands from optical to mid-infrared ($BP$, $G$, $RP$, $J$, $H$, $K_S$, $W1$, $W2$, $W3$) and in five Wesenheit bands based on Gaia DR3 parallaxes. The dispersion of PLZ relations gradually decreases from the optical to mid-infrared bands, with the minimum dispersion of 0.138 mag. We fit the best PL relations for three bands ($W1$, $W_{G,BP,RP}$, $W_{W1,BP,RP}$) under different parallax uncertainty criteria and determine a parallax (after correction) zero point of $zp_\varpi=24\pm4$ $μ$as. After fixing the parallax zero point, we find that the total zero errors of the PL and PLZ relation are smallest when the parallax uncertainty is less than 2\%, resulting in a calibrated CB luminosity with an accuracy of 0.33\%, which is more accurate than the classical Cepheids. Furthermore, by examining the absolute magnitude residuals in different metallicity intervals, we find that the use of a linear metallicity effect is appropriate for CBs with different metallicities. These results indicate that CBs are excellent standard candles.

astro-ph.SR↗

arXiv2025

On blowup solution in NLS equation under dispersion or nonlinearity management

In this paper, we study the dispersion-managed nonlinear Schrödinger (DM-NLS) equation $$ i\partial_t u(t,x)+γ(t)Δu(t,x)=|u(t,x)|^{\frac4d}u(t,x),\quad x\in\R^d, $$ and the nonlinearity-managed NLS (NM-NLS) equation: $$ i\partial_t u(t,x)+Δu(t,x)=γ(t)|u(t,x)|^{\frac4d}u(t,x), \quad x\in\R^d, $$ where $γ(t)$ is a periodic function which is equal to $-1$ when $t\in (0,1]$ and is equal to $1$ when $t\in (1,2]$. The two models share the feature that the focusing and defocusing effects convert periodically. For the classical focusing NLS, it is known that the initial data $$ u_0(x)=T^{-\frac{d}{2}}\fe^{i\frac{|x|^2}{4T} -i\frac{ω^2}{T}}Q_ω\left(\frac{x}{T}\right) $$ leads to a blowup solution $$(T-t)^{-\frac{d}{2}}\fe^{i\frac{|x|^2}{4(T-t)} -i\frac{ω^2}{T-t}}Q_ω\left(\frac{x}{T-t}\right), $$ so when $T\leq1$, this is also a blowup solution for DM-NLS and NM-NLS which blows up in the first focusing layer. For DM-NLS, we prove that when $T>1$, the initial data $u_0$ above does not lead to a finite-time blowup and the corresponding solution is globally well-posed. For NM-NLS, we prove the global well-posedness for $T\in(1,2)$ and we construct solution that can blow up at any focusing layer. The theoretical studies are complemented by extensive numerical explorations towards understanding the stabilization effects in the two models and addressing their difference.

math.AP↗

arXiv2025

Machine Learning Analysis of Anomalous Diffusion

The rapid advancements in machine learning have made its application to anomalous diffusion analysis both essential and inevitable. This review systematically introduces the integration of machine learning techniques for enhanced analysis of anomalous diffusion, focusing on two pivotal aspects: single trajectory characterization via machine learning and representation learning of anomalous diffusion. We extensively compare various machine learning methods, including both classical machine learning and deep learning, used for the inference of diffusion parameters and trajectory segmentation. Additionally, platforms such as the Anomalous Diffusion Challenge that serve as benchmarks for evaluating these methods are highlighted. On the other hand, we outline three primary strategies for representing anomalous diffusion: the combination of predefined features, the feature vector from the penultimate layer of neural network, and the latent representation from the autoencoder, analyzing their applicability across various scenarios. This investigation paves the way for future research, offering valuable perspectives that can further enrich the study of anomalous diffusion and advance the application of artificial intelligence in statistical physics and biophysics.

cs.LG↗

arXiv2025

Petting Pen for Stress Awareness and Management in Children

We found that children in elementary school often experience stress during task performance. Limited coping skills and lack of stress awareness restrict children's ability to manage their stress. Many designs and studies have proposed different stress detection and intervention solutions. Still, they often overlook the potential of enhancing everyday objects and actively sensing stress-related behavioral data during human-product interaction. Therefore, we propose Petting pen as an interactive robotic object for children to manage their stress during task performance. It detects and validates stress and further intervenes in stress during a process of natural writing and relaxation interactions. The design is an iteration based on our previous research results of a stress-aware pen, enhanced with tactile needs, robotic interaction, and integration of behavioral and bio-sensing capabilities. Petting pen is supposed to bridge the gap between robots and everyday objects in mental health applications for children.

cs.HC↗

arXiv2025

Can Large Language Models Play Text Games Well? Current State-of-the-Art and Open Questions

Large language models (LLMs) such as ChatGPT and GPT-4 have recently demonstrated their remarkable abilities of communicating with human users. In this technical report, we take an initiative to investigate their capacities of playing text games, in which a player has to understand the environment and respond to situations by having dialogues with the game world. Our experiments show that ChatGPT performs competitively compared to all the existing systems but still exhibits a low level of intelligence. Precisely, ChatGPT can not construct the world model by playing the game or even reading the game manual; it may fail to leverage the world knowledge that it already has; it cannot infer the goal of each step as the game progresses. Our results open up new research questions at the intersection of artificial intelligence, machine learning, and natural language processing.

cs.CL↗

arXiv2025

Linking stellar populations to HII regions across nearby galaxies. II. Infrared Reprocessed and UV Direct Radiation Pressure in HII Regions

Radiation pressure is a key mechanism by which stellar feedback disrupts molecular clouds and drives HII region expansion. This includes direct radiation pressure exerted by UV photons on dust grains, pressure associated with photoionization, and infrared (IR) radiation pressure on grains due to dust-reprocessed IR photons. We present a new method that combines high resolution mid-IR luminosities from JWST-MIRI, optical attenuation and nebular line measurements from VLT-MUSE, and HST H$α$-based region sizes to estimate the strength of radiation pressure in $\approx 18,000$ HII regions across 19 nearby star-forming galaxies. This is the most extensive and direct estimate of these terms beyond the Local Group to date. In the disks of galaxies, we find that the total reprocessed IR pressure is on average 5% of the direct UV radiation pressure. This fraction rises to 10% in galaxy centers. We expect reprocessed IR radiation pressure to dominate over UV radiation pressure in regions where $L_{\rm F2100W}/L_{\rm Hα}^{\rm corr} \gtrsim 75$. Radiation pressure due to H ionizations is lower than pressure on dust in our sample, but appears likely to dominate the radiation pressure budget in dwarf galaxies similar to the Small Magellanic Cloud. The contribution from all radiation pressure terms appears to be subdominant compared to thermal pressure from ionized gas, reinforcing the view that radiation pressure is most important in compact, heavily embedded, and young regions.

astro-ph.GA↗

arXiv2025

Drone Remote Identification Based on Zadoff-Chu Sequences and Time-Frequency Images

We propose an algorithm based on Zadoff-Chu (ZC) sequences and time-frequency images (TFI) to achieve drone remote identification (RID). Specifically, by analyzing the modulation parameters and frame structures of drone ratio-frequency (RF) signals in the DroneRFa dataset, we extract prior information about ZC sequences with surprising correlation properties and robustness. Cross-correlation is performed between locally generated ZC sequences and drone signals to derive ZC sequence-based features. Then, these ZF sequence features are fused with TFI features containing communication protocol information to achieve drone RID. To reduce computational costs, data reduction of the cross-correlation features is performed by analyzing the frame structures and modulation parameters, ensuring that the feature performance remained unaffected. Three feature fusion methods, namely probability-weighted addition, feature vector addition, and feature vector concatenation, are analyzed. Simulation results demonstrate that the proposed algorithm improves the average accuracy by at least 2.5\% compared to existing methods, which also indicate robust RID performance under burst interference and background noise. For RF sampling signals at varying flight distances, the proposed algorithm achieves a maximum accuracy of 99.11\%.

eess.SP↗

arXiv2025

FlexStep: Enabling Flexible Error Detection in Multi/Many-core Real-time Systems

Reliability and real-time responsiveness in safety-critical systems have traditionally been achieved using error detection mechanisms, such as LockStep, which require pre-configured checker cores,strict synchronisation between main and checker cores, static error detection regions, or limited preemption capabilities. However, these core-bound hardware mechanisms often lead to significant resource over-provisioning, and diminished real-time responsiveness, particularly in modern systems where tasks with varying reliability requirements are consolidated on shared processors to improve efficiency, reduce costs, and save power. To address these challenges, this work presents FlexStep, a systematic solution that integrates hardware and software across the SoC, ISA, and OS scheduling layers. FlexStep features a novel microarchitecture that supports dynamic core configuration and asynchronous, preemptive error detection. The FlexStep architecture naturally allows for flexible task scheduling and error detection, enabling new scheduling algorithms that enhance both resource efficiency and real-time schedulability. We publicly release FlexStep's source code, at https://anonymous.4open.science/r/FlexStep-DAC25-7B0C.

cs.AR↗

arXiv2025

Revisiting the Extraction of Coupling Strength for Polaron Hopping from $ab~initio$ Approach

Accurately determining the coupling strength between polaron states is essential to describe charge-hopping transport in materials. In this work, we revisit methodologies to extract coupling strengths using ab initio approaches. Our findings underscore the critical role of incorporating anharmonic effects in the model Hamiltonian when analyzing total energy variations along reaction coordinates. Furthermore, we demonstrate that coupling strength extraction based on total energy calculated from ab initio approaches is fundamentally limited due to the stabilization of diabatic states, which do not involve coupling strength. We demonstrate that such limitation exists in both DFT+U and HSE hybrid functionals, which are widely used in the study of polaron transport. Instead, we suggest extracting coupling strength directly from the electronic structure of a system in neutral conditions. The neutral condition avoids overestimating coupling strength due to additional energy splitting between bonding and anti-bonding states from the charging energy. This study highlights the limitations of existing methods and introduces a robust framework for accurately extracting coupling parameters, paving the way for improved modeling of charge transport in complex materials.

cond-mat.mtrl-sci↗

arXiv2025

DTGBrepGen: A Novel B-rep Generative Model through Decoupling Topology and Geometry

Boundary representation (B-rep) of geometric models is a fundamental format in Computer-Aided Design (CAD). However, automatically generating valid and high-quality B-rep models remains challenging due to the complex interdependence between the topology and geometry of the models. Existing methods tend to prioritize geometric representation while giving insufficient attention to topological constraints, making it difficult to maintain structural validity and geometric accuracy. In this paper, we propose DTGBrepGen, a novel topology-geometry decoupled framework for B-rep generation that explicitly addresses both aspects. Our approach first generates valid topological structures through a two-stage process that independently models edge-face and edge-vertex adjacency relationships. Subsequently, we employ Transformer-based diffusion models for sequential geometry generation, progressively generating vertex coordinates, followed by edge geometries and face geometries which are represented as B-splines. Extensive experiments on diverse CAD datasets show that DTGBrepGen significantly outperforms existing methods in both topological validity and geometric accuracy, achieving higher validity rates and producing more diverse and realistic B-reps. Our code is publicly available at https://github.com/jinli99/DTGBrepGen.

cs.CV↗

arXiv2025

Step-Video-TI2V Technical Report: A State-of-the-Art Text-Driven Image-to-Video Generation Model

We present Step-Video-TI2V, a state-of-the-art text-driven image-to-video generation model with 30B parameters, capable of generating videos up to 102 frames based on both text and image inputs. We build Step-Video-TI2V-Eval as a new benchmark for the text-driven image-to-video task and compare Step-Video-TI2V with open-source and commercial TI2V engines using this dataset. Experimental results demonstrate the state-of-the-art performance of Step-Video-TI2V in the image-to-video generation task. Both Step-Video-TI2V and Step-Video-TI2V-Eval are available at https://github.com/stepfun-ai/Step-Video-TI2V.

cs.CV↗

arXiv2025

The infrared traced star formation rates of associated absorption lines quasars

Some optically selected quasars exhibit Mg II assoicated absorption lines (AALs), and its origin remains unclear. In this paper, we compile a sample of 1769 quasars, with or without Mg II AALs. Of which 1689 are Far-Infrared (FIR) detected quasars and the rest are not detected in FIR. For the FIR undetected quasars, we obtain stacks for both with and without Mg II AAL quasars. Then we estimate the star formation rates (SFRs) within quasar host galaxies based on their FIR luminosities derived from their FIR greybody components, and find that, although quasars with Mg II AALs have significantly redder median composite spectra than those without Mg II AALs, the SFR distributions of the two types of quasars are statistically indistinguishable. These results do not require an evolutionary link between the quasars with and without Mg II AALs, and would be reconciled if an orientation effect cannot be ignored among the quasars hosting different types of absorption lines.

astro-ph.GA↗

arXiv2025

New routes for PN destruction and formation in the ISM via neutral-neutral gas-phase reactions and an extended database for reactions involving phosphorus

Phosphorus plays an essential role in the chemistry of living organisms, being present in several fundamental biomolecules. The investigation of chemical reactions taking place in different astronomical environments involving phosphorus-containing molecules is essential for understanding how these species are produced and destroyed. Phosphorus monoxide (PO) and phosphorus nitride (PN) are key reservoirs of phosphorus in the Interstellar Medium (ISM). This work presents a computational study of the CPN system to identify viable reaction pathways involving atom-diatom collisions and to explore a potential destruction route for PN in the ISM. We explore the potential energy landscape of the C($\mathrm{^3P}$) + PN($^1Σ^+$), N($\mathrm{^4S}$) + CP($^2Σ^+$) and P($\mathrm{^4S}$) + CN($^2Σ^+$) reactions by performing high-accuracy ab initio calculations and provide their rate coefficients over a wide range of temperatures. The temperature-dependent rate coefficients were fitted to the modified Arrhenius equation: $k(T)=α(T/300)^β\mathrm{exp}(-γ/T)$. An updated chemical network for P-bearing species was used to model the time-dependent abundances and reaction contributions of P, PO, PN, and PH during the chemical evolution of diffuse/translucent and dense clouds. The only neutral-neutral reaction capable of destroying PN without an activation energy seems to be the PN+C one. We have also shown that reactions between CP and N can yield CN and PN barrierless. Chemical models indicate that PO is a crucial species driving the gas-phase formation of PN. Typically, PO/PN ratios exceed 1, though their chemistry is influenced by photon- and cosmic-ray-induced processes. Over time in simulated dense clouds, neutral-neutral reactions such as PO + N, PH + N, P + OH, and PH + O play a significant role in determining the relative abundances of PO and PN.

astro-ph.GA↗

arXiv2025

Calibrating the Color-Magnitude Relation of M Giants by Using Open Clusters

M giants, with their distinctive properties such as high luminosity, serve as excellent indicators for mapping the structure of the Milky Way. The distance to distant M giants can be determined by using the color-magnitude relation (CMR), which is derived from color-magnitude diagrams of specific systems in previous studies. In this work, we aimed to achieve more accurate distance determination for M giants by focusing on open clusters (OCs) with a large number of member stars and thus improve the CMR. For the first time, we compiled a census of OCs harboring M giants using Gaia Data Release 3 (DR3) and Large Sky Area Multi-Object Fiber Spectroscopic Telescope Data Release 9. We identified 58 M giants associated with 43 OCs and obtained their astrometric and photometric parameters from Gaia DR3. Using the distances of these OCs, we derived the CMR for M giants as a linear correlation, expressed as $M_{Ks}=3.85-8.26(J-K_s$). This linear relation proved superior to the empirical distance relation in characterizing the CMR of M giants. The photometric distances of M giants derived from the CMR are consistent with the parallax distances from Gaia and known spectroscopic distances, with median deviations of 1.5% and 2.3%, respectively. Using the distances of M giants derived from the CMR, we computed their radial velocity ($V_R$), azimuthal velocity ($Vϕ$), and vertical velocity ($V_Z$), respectively. The distributions of these velocities revealed key features of the Galactic disk, including oscillation, north-south rotational asymmetry, and warp. These findings are consistent with previous studies and further validate the reliability of the derived CMR.

astro-ph.SR↗

arXiv2025

Giant Topological Hall Effect Across Wide Temperature in Pt/NiCo2O4 Heterostructure

Topological Hall effect (THE), a quantum phenomenon arising from emergent magnetic field generated by topological spin texture, is a key method for detecting non-coplanar spin structures like skyrmions in magnetic materials. Here, we investigate a bilayer structure of Pt and conducting ferrimagnet NiCo2O4 (NCO) of perpendicular magnetic anisotropy and demonstrate giant THE across a temperature range 2 - 350 K. The absence of THE in single-layer Pt and NCO, as well as in Pt/Cu/NCO, suggests its interfacial origin. The maximum THE occurring just before the NCO coercive field indicates its connection to magnetic nucleation centers, which are topologically equivalent to skyrmions. The large normalized THE, based on the emergent-field model, points to a high population density of small nucleation centers. This aligns with the unresolvable domain structures during magnetization reversal, even though clear domain structures are detected after zero-field cooling. These results establish heavy metal/NCO as a promising system for exploring topological spin structures.

cond-mat.mtrl-sci↗

arXiv2025

Extending the Width of Short Aperture Data by Data-Driven Extrapolation

It is commonly believed that it is infeasible to estimate the phase velocity of a surface wave with a recording aperture less than $λ/2$ in length, where $λ$ is the horizontal wavelength of the propagating wave. We show both theoretically and computationally that the width of the recording aperture can be extended using the recorded data at longer time intervals. This leads to more reliable estimates of apparent velocities for both body waves and surface waves. Here, we assume single-mode plane waves sweeping across the recording array with a linear moveout.

physics.geo-ph↗

arXiv2025

Exploring Hierarchical Molecular Graph Representation in Multimodal LLMs

Following the milestones in large language models (LLMs) and multimodal models, we have seen a surge in applying LLMs to biochemical tasks. Leveraging graph features and molecular text representations, LLMs can tackle various tasks, such as predicting chemical reaction outcomes and describing molecular properties. However, most current work overlooks the *multi-level nature* of the graph modality, even though different chemistry tasks may benefit from different feature levels. In this work, we first study the effect of feature granularity and reveal that even reducing all GNN-generated feature tokens to a single one does not significantly impact model performance. We then investigate the effect of various graph feature levels and demonstrate that both the quality of LLM-generated molecules and model performance across different tasks depend on different graph feature levels. Therefore, we conclude with two key insights: (1) current molecular-related multimodal LLMs lack a comprehensive understanding of graph features, and (2) static processing is not sufficient for hierarchical graph feature. We share our findings in detail, with the hope of paving the way for the community to develop more advanced multimodal LLMs for incorporating molecular graphs.

cs.LG↗

arXiv2025

Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in Large Language Models

Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks but their performance in complex logical reasoning tasks remains unsatisfactory. Although some prompting methods, such as Chain-of-Thought, can improve the reasoning ability of LLMs to some extent, they suffer from an unfaithful issue where derived conclusions may not align with the generated reasoning chain. To address this issue, some studies employ the approach of propositional logic to further enhance logical reasoning abilities of LLMs. However, the potential omissions in the extraction of logical expressions in these methods can cause information loss in the logical reasoning process, thereby generating incorrect results. To this end, we propose Logic-of-Thought (LoT) prompting which employs propositional logic to generate expanded logical information descriptions and utilizes them as an additional augmentation to original contexts, thereby ensuring information completeness and enhancing logical reasoning ability. LoT is orthogonal to existing prompting methods and can be seamlessly integrated with them. Extensive experiments demonstrate that LoT boosts the performance of various prompting methods with a striking margin across five logical reasoning tasks. In particular, LoT enhances Chain-of-Thought's performance on the ReClor dataset by +4.35%, improves Chain-of-Thought with Self-Consistency's performance on the RuleTaker dataset by +3.52%, and boosts performance of Tree-of-Thoughts on the ProofWriter dataset by +8%.

cs.CL↗