arXiv subjects

Qi Song

Publications and source records attributed to Qi Song.

At least 55 records · Page 3Linked to original sources

arXiv2025

Condensation of Composite Bosonic Trions in Interacting Bose-Fermi Mixtures

We reveal a quantum coherent state characterized by composite bosonic trions, wherein paired fermions further bind with bosons, in one-dimensional Bose-Fermi mixtures.This phase emerges in two separate models, both featuring onsite boson-fermion attraction that induces negative binding energy for the composite trions. The first is the pair-hopping model, in which spinless fermions undergo pair hopping to form preformed pairs. The second is the extended Bose-Fermi Hubbard model, describing dipolar bosons and fermions with density-density interactions. Notably, the formation of composite trions is independent of the specific pairing interactions of fermions. Extensive density matrix renormalization group calculations demonstrate the quasi-condensation of the composite trions, evidenced by algebraically decaying correlations of trions, gapped single-particle excitations, and suppressed fermion pair correlations. Our findings provide valuable insights into the three-body pairing mechanism in mixed-particle systems.

cond-mat.quant-gas↗

arXiv2025

CrossLinear: Plug-and-Play Cross-Correlation Embedding for Time Series Forecasting with Exogenous Variables

Time series forecasting with exogenous variables is a critical emerging paradigm that presents unique challenges in modeling dependencies between variables. Traditional models often struggle to differentiate between endogenous and exogenous variables, leading to inefficiencies and overfitting. In this paper, we introduce CrossLinear, a novel Linear-based forecasting model that addresses these challenges by incorporating a plug-and-play cross-correlation embedding module. This lightweight module captures the dependencies between variables with minimal computational cost and seamlessly integrates into existing neural networks. Specifically, it captures time-invariant and direct variable dependencies while disregarding time-varying or indirect dependencies, thereby mitigating the risk of overfitting in dependency modeling and contributing to consistent performance improvements. Furthermore, CrossLinear employs patch-wise processing and a global linear head to effectively capture both short-term and long-term temporal dependencies, further improving its forecasting precision. Extensive experiments on 12 real-world datasets demonstrate that CrossLinear achieves superior performance in both short-term and long-term forecasting tasks. The ablation study underscores the effectiveness of the cross-correlation embedding module. Additionally, the generalizability of this module makes it a valuable plug-in for various forecasting tasks across different domains. Codes are available at https://github.com/mumiao2000/CrossLinear.

cs.LG↗

arXiv2025

KnowPath: Knowledge-enhanced Reasoning via LLM-generated Inference Paths over Knowledge Graphs

Large language models (LLMs) have demonstrated remarkable capabilities in various complex tasks, yet they still suffer from hallucinations. By incorporating and exploring external knowledge, such as knowledge graphs(KGs), LLM's ability to provide factual answers has been enhanced. This approach carries significant practical implications. However, existing methods suffer from three key limitations: insufficient mining of LLMs' internal knowledge, constrained generation of interpretable reasoning paths, and unclear fusion of internal and external knowledge. Therefore, we propose KnowPath, a knowledge-enhanced large model framework driven by the collaboration of internal and external knowledge. It relies on the internal knowledge of the LLM to guide the exploration of interpretable directed subgraphs in external knowledge graphs, better integrating the two knowledge sources for more accurate reasoning. Extensive experiments on multiple real-world datasets demonstrate the effectiveness of KnowPath. Our code and data are available at https://github.com/tize-72/KnowPath.

cs.AI↗

arXiv2025

TeleEval-OS: Performance evaluations of large language models for operations scheduling

The rapid advancement of large language models (LLMs) has significantly propelled progress in artificial intelligence, demonstrating substantial application potential across multiple specialized domains. Telecommunications operation scheduling (OS) is a critical aspect of the telecommunications industry, involving the coordinated management of networks, services, risks, and human resources to optimize production scheduling and ensure unified service control. However, the inherent complexity and domain-specific nature of OS tasks, coupled with the absence of comprehensive evaluation benchmarks, have hindered thorough exploration of LLMs' application potential in this critical field. To address this research gap, we propose the first Telecommunications Operation Scheduling Evaluation Benchmark (TeleEval-OS). Specifically, this benchmark comprises 15 datasets across 13 subtasks, comprehensively simulating four key operational stages: intelligent ticket creation, intelligent ticket handling, intelligent ticket closure, and intelligent evaluation. To systematically assess the performance of LLMs on tasks of varying complexity, we categorize their capabilities in telecommunications operation scheduling into four hierarchical levels, arranged in ascending order of difficulty: basic NLP, knowledge Q&A, report generation, and report analysis. On TeleEval-OS, we leverage zero-shot and few-shot evaluation methods to comprehensively assess 10 open-source LLMs (e.g., DeepSeek-V3) and 4 closed-source LLMs (e.g., GPT-4o) across diverse scenarios. Experimental results demonstrate that open-source LLMs can outperform closed-source LLMs in specific scenarios, highlighting their significant potential and value in the field of telecommunications operation scheduling.

cs.CL↗

arXiv2025

Unified Steganography via Implicit Neural Representation

Digital steganography is the practice of concealing for encrypted data transmission. Typically, steganography methods embed secret data into cover data to create stega data that incorporates hidden secret data. However, steganography techniques often require designing specific frameworks for each data type, which restricts their generalizability. In this paper, we present U-INR, a novel method for steganography via Implicit Neural Representation (INR). Rather than using the specific framework for each data format, we directly use the neurons of the INR network to represent the secret data and cover data across different data types. To achieve this idea, a private key is shared between the data sender and receivers. Such a private key can be used to determine the position of secret data in INR networks. To effectively leverage this key, we further introduce a key-based selection strategy that can be used to determine the position within the INRs for data storage. Comprehensive experiments across multiple data types, including images, videos, audio, and SDF and NeRF, demonstrate the generalizability and effectiveness of U-INR, emphasizing its potential for improving data security and privacy in various applications.

cs.CR↗

arXiv2025

Magnetic excitations in Nd$_{n+1}$Ni$_{n}$O$_{3n+1}$ Ruddlesden-Popper nickelates observed via resonant inelastic x-ray scattering

Magnetic interactions are thought to play a key role in the properties of many unconventional superconductors, including cuprates, iron pnictides, and square-planar nickelates. Superconductivity was also recently observed in the bilayer and trilayer Ruddlesden-Popper nickelates, whose electronic structure is expected to differ from that of cuprates and square-planar nickelates. Here we study how electronic structure and magnetic interactions evolve with the number of layers, $n$, in thin film Ruddlesden-Popper nickelates Nd$_{n+1}$Ni$_{n}$O$_{3n+1}$ with $n=1,\:3$, and 5 using resonant inelastic x-ray scattering (RIXS). The RIXS spectra are consistent with a high-spin $|3d^8 \underline{L} \rangle$ electronic configuration, resembling that of La$_{2-x}$Sr$_x$NiO$_4$ and the parent perovskite, NdNiO$_3$. The magnetic excitations soften to lower energy in the structurally self-doped, higher-$n$ films. Our observations confirm that structural tuning is an effective route for altering electronic properties, such as magnetic superexchange, in this prominent family of materials.

cond-mat.str-el↗

arXiv2025

Floquet-Engineered Hybrid Topological Orders with Majorana Edge Modes in Number-Conserving Fermionic Quantum Simulators

We develop an experimental protocol based on Floquet-engineered ultracold fermions in optical lattices, enabling the emulation of pair-hopping and competing singlet/triplet pairing interactions. Through large-scale density matrix renormalization group (DMRG) simulations, we uncover three emergent topological phases: (i) A Majorana-enabled spin-density-wave (MS) phase featuring exponentially localized edge charges, non-local fermionic edge correlations, and doubly degenerate entanglement spectra; (ii) A z-axis polarized triplet superconducting (TS) phase exhibiting fractionalized edge spins (S=1/4 per edge), two-fold ground state degeneracy and a bulk single-particle gap; (iii) A hybrid x-directional triplet superconducting (XTS) phase that uniquely combines fractional spin textures and Majorana-type edge correlations, defining a new universality class of hybrid orders in number-conserving systems. These findings establish a universal framework for engineering non-Abelian topological matter, crucially bypassing the need for external pairing fields while maintaining experimental feasibility with current cold-atom techniques.

cond-mat.quant-gas↗

arXiv2025

Unleashing the Potential of Two-Tower Models: Diffusion-Based Cross-Interaction for Large-Scale Matching

Two-tower models are widely adopted in the industrial-scale matching stage across a broad range of application domains, such as content recommendations, advertisement systems, and search engines. This model efficiently handles large-scale candidate item screening by separating user and item representations. However, the decoupling network also leads to a neglect of potential information interaction between the user and item representations. Current state-of-the-art (SOTA) approaches include adding a shallow fully connected layer(i.e., COLD), which is limited by performance and can only be used in the ranking stage. For performance considerations, another approach attempts to capture historical positive interaction information from the other tower by regarding them as the input features(i.e., DAT). Later research showed that the gains achieved by this method are still limited because of lacking the guidance on the next user intent. To address the aforementioned challenges, we propose a "cross-interaction decoupling architecture" within our matching paradigm. This user-tower architecture leverages a diffusion module to reconstruct the next positive intention representation and employs a mixed-attention module to facilitate comprehensive cross-interaction. During the next positive intention generation, we further enhance the accuracy of its reconstruction by explicitly extracting the temporal drift within user behavior sequences. Experiments on two real-world datasets and one industrial dataset demonstrate that our method outperforms the SOTA two-tower models significantly, and our diffusion approach outperforms other generative models in reconstructing item representations.

cs.IR↗

arXiv2025

The Evolution of Size and Merger Fraction of Submillimeter Galaxies across $1 < z \lesssim 6$ as Observed by JWST

Precise tracking of the growth in galaxy size and the evolution of merger fractions with redshift is vital for understanding the formation history of submillimeter galaxies (SMGs). This study investigates these evolutions over a broad redshift range ($1 < z \lesssim 6$), using a sample of 222 SMGs with a median redshift of $z = 2.61^{+0.89}_{-0.82}$ identified by ALMA and JCMT, enhanced by the advanced imaging capabilities of the JWST/NIRCam and MIRI. We find significant evolution in effective radii ($R_e$) in rest-frame V-band ($R_e \propto (1 + z)^{-0.87 \pm 0.08}$) and near-infrared (NIR) band ($R_e \propto (1 + z)^{-0.88 \pm 0.11}$), with the NIR size evolution resembling that of massive star-forming galaxies at lower redshift. Visual inspections reveal a major merger fraction of $24.3 \pm 3.7\%$ and an interaction fraction of up to $48.4 \pm 11.1\%$. The major merger fraction exhibits an increase from 14.7$\pm9.1$\% at $z = 1$ to 26.6$\pm 8.4$\% at $z = 3$, after which it remains approximately constant across the redshift range $3 < z < 6$. In contrast, the interaction fraction remains relatively stable across the range $2 < z < 5$. Our results indicate that late-stage major mergers are not the primary formation mechanism for SMGs at $z<3$, while interactions appear to play a significant role across the broader redshift range of $1<z<6$. Additionally, HST-based major merger identifications may overestimate the true fraction by a factor of 1.7 at $z \sim 2$. These findings highlight the varying roles of mergers and interactions in driving the formation of massive, dusty star-forming galaxies across different redshifts.

astro-ph.GA↗

arXiv2025

Valency, charge-transfer, and orbital-dependent correlation in bilayer nickelates Nd3Ni2O7

We examine the bulk electronic structure of Nd3Ni2O7 using Ni 2p core-level hard x-ray photoemission spectroscopy combined with density functional theory + dynamical mean-field theory. Our results reveal a large deviation of the Ni 3d occupation from the formal Ni2.5+ valency, highlighting the importance of the charge-transfer from oxygen ligands. We find that the dominant d8 configuration is accompanied by nearly equal contributions from d7 and d9 states, exhibiting an unusual valence state among Ni-based oxides. Finally, we discuss the Ni dx2-y2 and dz2 orbital-dependent hybridization, correlation and local spin dynamics.

cond-mat.str-el↗

arXiv2024

Geometry Cloak: Preventing TGS-based 3D Reconstruction from Copyrighted Images

Single-view 3D reconstruction methods like Triplane Gaussian Splatting (TGS) have enabled high-quality 3D model generation from just a single image input within seconds. However, this capability raises concerns about potential misuse, where malicious users could exploit TGS to create unauthorized 3D models from copyrighted images. To prevent such infringement, we propose a novel image protection approach that embeds invisible geometry perturbations, termed "geometry cloaks", into images before supplying them to TGS. These carefully crafted perturbations encode a customized message that is revealed when TGS attempts 3D reconstructions of the cloaked image. Unlike conventional adversarial attacks that simply degrade output quality, our method forces TGS to fail the 3D reconstruction in a specific way - by generating an identifiable customized pattern that acts as a watermark. This watermark allows copyright holders to assert ownership over any attempted 3D reconstructions made from their protected images. Extensive experiments have verified the effectiveness of our geometry cloak. Our project is available at https://qsong2001.github.io/geometry_cloak.

cs.CV↗

arXiv2024

Exotic Superfluid with Emergent flux in a one-dimensional Bose-Fermi mixture

We find a novel chiral superfluid (CSF) phase in a one-dimensional Bose-Fermi Hubbard model with significant mass and density imbalance between the two species. In the CSF phase, bosons condensate at non-zero momentum $\pm 2π/L$ with chain length $L$. To capture the essential physics of this new phenomenon, we study an alternative simplified model that only features competition between single-fermion hopping and hopping of composite particles composed of a fermion and a boson. This model captures the low energy physics of the Hubbard model and hosts a robust CSF phase. Our unbiased numerical studies show that in the CSF phase, the local superfluid order parameter continuously rotates along the chain, indicating that time-reversal symmetry is spontaneously broken. This symmetry breaking generates an emergent flux in the background, effectively optimizing the system's ground-state energy. We provide a physical understanding at the mean-field level. Furthermore, we have explored the potential realization of this phase in cold-atom experiments.

cond-mat.quant-gas↗

arXiv2024

Enhancing Playback Performance in Video Recommender Systems with an On-Device Gating and Ranking Framework

Video recommender systems (RSs) have gained increasing attention in recent years. Existing mainstream RSs focus on optimizing the matching function between users and items. However, we noticed that users frequently encounter playback issues such as slow loading or stuttering while browsing the videos, especially in weak network conditions, which will lead to a subpar browsing experience, and may cause users to leave, even when the video content and recommendations are superior. It is quite a serious issue, yet easily overlooked. To tackle this issue, we propose an on-device Gating and Ranking Framework (GRF) that cooperates with server-side RS. Specifically, we utilize a gate model to identify videos that may have playback issues in real-time, and then we employ a ranking model to select the optimal result from a locally-cached pool to replace the stuttering videos. Our solution has been fully deployed on Kwai, a large-scale short video platform with hundreds of millions of users globally. Moreover, it significantly enhances video playback performance and improves overall user experience and retention rates.

cs.IR↗

arXiv2024

Theoretical study on the core-excited states of the allyl using CVS-icMRCISD method

The allyl radical (C3H5) is a well-characterized hydrocarbon radical, renowned for its pivotal role as an intermediate species in high-energy environments. Its core excited states can elucidate intricate details pertaining to its electronic and structural properties. The core excited states of allyl were studied experimentally using X-ray absorption spectroscopy (XAS), and the primary characteristic peaks were assigned using the MCSCF approach, but not entirely. In this work, the recently developed CVS-icMRCISD scheme was used to simulate the excitation and ionization processes of C's K-shell electrons within allyl radicals, cations, and anions, respectively. Our results indicate that the XAS spectrum obtained not merely captured the distinctive peaks associated with allyl radicals, but also encompassed the characteristic peaks pertaining to allyl cations. Meanwhile, unlike manually adjusting the state weights of different electronic states to align with experimental spectral data, we adopt the CVS-icMRCISD scheme, which uses state averaging and produces unbiased results, making it suitable for studying multiple states simultaneously and easy to converge. More importantly, when accounting for the dynamic electron correlation, our results align seamlessly with the experimental XAS. This congruence underscores the potential of our CVS-icMRCISD as a robust tool for theoretical investigations pertaining to the excitation of inner shell electrons in small molecules.

physics.chem-ph↗

arXiv2024

Divide and Conquer: Improving Multi-Camera 3D Perception with 2D Semantic-Depth Priors and Input-Dependent Queries

3D perception tasks, such as 3D object detection and Bird's-Eye-View (BEV) segmentation using multi-camera images, have drawn significant attention recently. Despite the fact that accurately estimating both semantic and 3D scene layouts are crucial for this task, existing techniques often neglect the synergistic effects of semantic and depth cues, leading to the occurrence of classification and position estimation errors. Additionally, the input-independent nature of initial queries also limits the learning capacity of Transformer-based models. To tackle these challenges, we propose an input-aware Transformer framework that leverages Semantics and Depth as priors (named SDTR). Our approach involves the use of an S-D Encoder that explicitly models semantic and depth priors, thereby disentangling the learning process of object categorization and position estimation. Moreover, we introduce a Prior-guided Query Builder that incorporates the semantic prior into the initial queries of the Transformer, resulting in more effective input-aware queries. Extensive experiments on the nuScenes and Lyft benchmarks demonstrate the state-of-the-art performance of our method in both 3D object detection and BEV segmentation tasks.

cs.CV↗

arXiv2024

Protecting NeRFs' Copyright via Plug-And-Play Watermarking Base Model

Neural Radiance Fields (NeRFs) have become a key method for 3D scene representation. With the rising prominence and influence of NeRF, safeguarding its intellectual property has become increasingly important. In this paper, we propose \textbf{NeRFProtector}, which adopts a plug-and-play strategy to protect NeRF's copyright during its creation. NeRFProtector utilizes a pre-trained watermarking base model, enabling NeRF creators to embed binary messages directly while creating their NeRF. Our plug-and-play property ensures NeRF creators can flexibly choose NeRF variants without excessive modifications. Leveraging our newly designed progressive distillation, we demonstrate performance on par with several leading-edge neural rendering methods. Our project is available at: \url{https://qsong2001.github.io/NeRFProtector}.

cs.CV↗

arXiv2024

Calibrating non-parametric morphological indicators from {\it JWST} images for galaxies over $0.5<z<3$

The measurements of morphological indicators of galaxies are often influenced by a series of observational effects. In this study, we utilize a sample of over 800 TNG50 simulated galaxies with log($M_*$/M$_\odot$)$>9$ at $0.5 1$\,$μ$m. The morphological indicators of star-forming galaxies (SFGs) and quiescent galaxies (QGs) are significantly different. The morphologies of QGs exhibit a higher sensitivity to rest-frame wavelength than SFGs. After analyzing the evolution of morphological indicators in the rest-frame V-band (0.5-0.7\,$μ$m) and rest-frame J-band (1.1-1.4\,$μ$m), we find that the morphologies of QGs evolve substantially with both redshift and stellar mass. For SFGs, the $C$, $Gini$ and $M_{\rm 20}$ show a rapid evolution with stellar mass at log($M_*$/M$_\odot$)$\geq10.5$, while the $A_{\rm O}$, $D_{\rm O}$ and $A$ evolve with both redshift and stellar mass. Our comparison shows that TNG50 simulations effectively reproduce the morphological indicators we measured from {\it JWST} observations when the impact of dust attenuation is considered.

astro-ph.GA↗

arXiv2024

Hydrogen is not necessary for superconductivity in topotactically reduced nickelates

A key open question in the study of layered superconducting nickelate films is the role that hydrogen incorporation into the lattice plays in the appearance of the superconducting state. Due to the challenges of stabilizing highly crystalline square planar nickelate films, films are prepared by the deposition of a more stable parent compound which is then transformed into the target phase via a topotactic reaction with a strongly reducing agent such as CaH$_2$. Recent studies, both experimental and theoretical, have introduced the possibility that the incorporation of hydrogen from the reducing agent into the nickelate lattice may be critical for the superconductivity. In this work, we use secondary ion mass spectrometry to examine superconducting La$_{1-x}$X$_x$NiO$_2$ / SrTiO$_3$ (X = Ca and Sr) and Nd$_6$Ni$_5$O$_{12}$ / NdGaO$_3$ films, along with non-superconducting NdNiO$_2$ / SrTiO$_3$ and (Nd,Sr)NiO$_2$ / SrTiO$_3$. We find no evidence for extensive hydrogen incorporation across a broad range of samples, including both superconducting and non-superconducting films. Theoretical calculations indicate that hydrogen incorporation is broadly energetically unfavorable in these systems, supporting our conclusion that hydrogen incorporation is not generally required to achieve a superconducting state in layered square-planar nickelates.

cond-mat.supr-con↗