arXiv subjects

Yixiao Zhang

Publications and source records attributed to Yixiao Zhang.

At least 37 records · Page 2Linked to original sources

arXiv2025

Temporal Adaptation of Pre-trained Foundation Models for Music Structure Analysis

Audio-based music structure analysis (MSA) is an essential task in Music Information Retrieval that remains challenging due to the complexity and variability of musical form. Recent advances highlight the potential of fine-tuning pre-trained music foundation models for MSA tasks. However, these models are typically trained with high temporal feature resolution and short audio windows, which limits their efficiency and introduces bias when applied to long-form audio. This paper presents a temporal adaptation approach for fine-tuning music foundation models tailored to MSA. Our method enables efficient analysis of full-length songs in a single forward pass by incorporating two key strategies: (1) audio window extension and (2) low-resolution adaptation. Experiments on the Harmonix Set and RWC-Pop datasets show that our method significantly improves both boundary detection and structural function prediction, while maintaining comparable memory usage and inference speed.

cs.SD↗

arXiv2025

Experience-Centric Resource Management in ISAC Networks: A Digital Agent-Assisted Approach

In this paper, we propose a digital agent (DA)-assisted resource management scheme for enhanced user quality of experience (QoE) in integrated sensing and communication (ISAC) networks. Particularly, user QoE is a comprehensive metric that integrates quality of service (QoS), user behavioral dynamics, and environmental complexity. The novel DA module includes a user status prediction model, a QoS factor selection model, and a QoE fitting model, which analyzes historical user status data to construct and update user-specific QoE models. Users are clustered into different groups based on their QoE models. A Cramér-Rao bound (CRB) model is utilized to quantify the impact of allocated communication resources on sensing accuracy. A joint optimization problem of communication and computing resource management is formulated to maximize long-term user QoE while satisfying CRB and resource constraints. A two-layer data-model-driven algorithm is developed to solve the formulated problem, where the top layer utilizes an advanced deep reinforcement learning algorithm to make group-level decisions, and the bottom layer uses convex optimization techniques to make user-level decisions. Simulation results based on a real-world dataset demonstrate that the proposed DA-assisted resource management scheme outperforms benchmark schemes in terms of user QoE.

eess.SY↗

arXiv2025

Hybrid Near-Far Field 6D Movable Antenna Design Exploiting Directional Sparsity and Deep Learning

Six-dimensional movable antenna (6DMA) has been identified as a new disruptive technology for future wireless systems to support a large number of users with only a few antennas. However, the intricate relationships between the signal carrier wavelength and the transceiver region size lead to inaccuracies in traditional far-field 6DMA channel model, causing discrepancies between the model predictions and the hybrid-field channel characteristics in practical 6DMA systems, where users might be in the far-field region relative to the antennas on the same 6DMA surface, while simultaneously being in the near-field region relative to different 6DMA surfaces. Moreover, due to the high-dimensional channel and the coupled position and rotation constraints, the estimation of the 6DMA channel and the joint design of the 6DMA positions and rotations and the transmit beamforming at the base station (BS) incur extremely high computational complexity. To address these issues, we propose an efficient hybrid-field generalized 6DMA channel model, which accounts for planar-wave propagation within individual 6DMA surfaces and spherical-wave propagation among different 6DMA surfaces. Furthermore, by leveraging directional sparsity, we propose a low-overhead channel estimation algorithm that efficiently constructs a complete channel map for all potential antenna position-rotation pairs while limiting the training overhead incurred by antenna movement. In addition, we propose a low-complexity design leveraging deep reinforcement learning (DRL), which facilitates the joint design of the 6DMA positions, rotations, and beamforming in a unified manner. Numerical results demonstrate that the proposed hybrid-field channel model and channel estimation algorithm outperform existing approaches and that the DRL-enhanced 6DMA system significantly surpasses flexible antenna systems.

cs.IT↗

arXiv2025

Separating hypergraph Turán densities

Determining the Turán densities of hypergraphs is a notoriously difficult problem at the core of combinatorics. Although Turán posed this problem in 1941, $π(K_{\ell}^{(k)})$ remains unknown for all $\ell>k\geq 3$. Prior to this work, it was not even known whether $π(K_{\ell}^{(k)})<π(K_{\ell+1}^{(k)})$ holds for general $\ell$ and $k$, and the best-known bounds on $π(K_{\ell}^{(k)})$ are far from implying anything close to this. We prove that $π(K_{\ell}^{(k)})<π(K_{\ell+1}^{(k)})$, for all $\ell>k\geq 3$, and provide a general criterion to distinguish the Turán densities of two hypergraphs. As a corollary, we obtain that $π(K_{k+1}^{(k)})<π(K_{k+2}^{(k)-})$, for all $k\geq 3$. For $k=3$, this was previously proved by Markström, answering a question by Erdős.

math.CO↗

arXiv2025

Density Hajnal--Szemerédi theorem for cliques of size four

The celebrated Corrádi--Hajnal Theorem~\cite{CH63} and the Hajnal--Szemerédi Theorem~\cite{HS70} determined the exact minimum degree thresholds for a graph on $n$ vertices to contain $k$ vertex-disjoint copies of $K_r$, for $r=3$ and general $r \ge 4$, respectively. The edge density version of the Corrádi--Hajnal Theorem was established by Allen--Böttcher--Hladký--Piguet~\cite{ABHP15} for large $n$. Remarkably, they determined the four classes of extremal constructions corresponding to different intervals of $k$. They further proposed the natural problem of establishing a density version of the Hajnal--Szemerédi Theorem: For $r \ge 4$, what is the edge density threshold that guarantees a graph on $n$ vertices contains $k$ vertex-disjoint copies of $K_r$ for $k \le n/r$. They also remarked, ``We are not even sure what the complete family of extremal graphs should be.'' We take the first step toward this problem by determining asymptotically the five classes of extremal constructions for $r=4$. Furthermore, we propose a candidate set comprising $r+1$ classes of extremal constructions for general $r \ge 5$.

math.CO↗

arXiv2024

Improving Controllability and Editability for Pretrained Text-to-Music Generation Models

The field of AI-assisted music creation has made significant strides, yet existing systems often struggle to meet the demands of iterative and nuanced music production. These challenges include providing sufficient control over the generated content and allowing for flexible, precise edits. This thesis tackles these issues by introducing a series of advancements that progressively build upon each other, enhancing the controllability and editability of text-to-music generation models. First, we introduce Loop Copilot, a system that tries to address the need for iterative refinement in music creation. Loop Copilot leverages a large language model (LLM) to coordinate multiple specialised AI models, enabling users to generate and refine music interactively through a conversational interface. Central to this system is the Global Attribute Table, which records and maintains key musical attributes throughout the iterative process, ensuring that modifications at any stage preserve the overall coherence of the music. While Loop Copilot excels in orchestrating the music creation process, it does not directly address the need for detailed edits to the generated content. To overcome this limitation, MusicMagus is presented as a further solution for editing AI-generated music. MusicMagus introduces a zero-shot text-to-music editing approach that allows for the modification of specific musical attributes, such as genre, mood, and instrumentation, without the need for retraining. By manipulating the latent space within pre-trained diffusion models, MusicMagus ensures that these edits are stylistically coherent and that non-targeted attributes remain unchanged. This system is particularly effective in maintaining the structural integrity of the music during edits, but it encounters challenges with more complex and real-world audio scenarios. ...

cs.SD↗

arXiv2024

Security Enhancement of Quantum Communication in Space-Air-Ground Integrated Networks

This paper investigates a transmission scheme for enhancing quantum communication security, aimed at improving the security of space-air-ground integrated networks (SAGIN). Quantum teleportation achieves the transmission of quantum states through quantum channels. In simple terms, an unknown quantum state at one location can be reconstructed on a particle at another location. By combining classical Turbo coding with quantum Shor error-correcting codes, we propose a practical solution that ensures secure information transmission even in the presence of errors in both classical and quantum channels. To provide absolute security under SAGIN, we add a quantum secure direct communication (QSDC) protocol to the current system. Specifically, by accounting for the practical scenario of eavesdropping in quantum channels, the QSDC protocol utilizes virtual entangled pairs to detect the presence of eavesdroppers. Consequently, the overall scheme guarantees both the reliability and absolute security of communication.

quant-ph↗

arXiv2024

Toward a density Corrádi--Hajnal theorem for degenerate hypergraphs

Given an $r$-graph $F$ with $r \ge 2$, let $\mathrm{ex}(n, (t+1) F)$ denote the maximum number of edges in an $n$-vertex $r$-graph with at most $t$ pairwise vertex-disjoint copies of $F$. Extending several old results and complementing prior work [J. Hou, H. Li, X. Liu, L.-T. Yuan, and Y. Zhang. A step towards a general density Corrádi--Hajnal theorem. arXiv:2302.09849, 2023.] on nondegenerate hypergraphs, we initiate a systematic study on $\mathrm{ex}(n, (t+1) F)$ for degenerate hypergraphs $F$. For a broad class of degenerate hypergraphs $F$, we present near-optimal upper bounds for $\mathrm{ex}(n, (t+1) F)$ when $n$ is sufficiently large and $t$ lies in intervals $\left[0, \frac{\varepsilon \cdot \mathrm{ex}(n,F)}{n^{r-1}}\right]$, $\left[\frac{\mathrm{ex}(n,F)}{\varepsilon n^{r-1}}, \varepsilon n \right]$, and $\left[ (1-\varepsilon)\frac{n}{v(F)}, \frac{n}{v(F)} \right]$, where $\varepsilon > 0$ is a constant depending only on $F$. Our results reveal very different structures for extremal constructions across the three intervals, and we provide characterizations of extremal constructions within the first interval. Additionally, for graphs, we offer a characterization of extremal constructions within the second interval. Our proof for the first interval also applies to a special class of nondegenerate hypergraphs, including those with undetermined Turán densities, partially improving a result in [J. Hou, H. Li, X. Liu, L.-T. Yuan, and Y. Zhang. A step towards a general density Corrádi--Hajnal theorem. arXiv:2302.09849, 2023.]

math.CO↗

arXiv2024

Content-based Controls For Music Large Language Modeling

Recent years have witnessed a rapid growth of large-scale language models in the domain of music audio. Such models enable end-to-end generation of higher-quality music, and some allow conditioned generation using text descriptions. However, the control power of text controls on music is intrinsically limited, as they can only describe music indirectly through meta-data (such as singers and instruments) or high-level representations (such as genre and emotion). We aim to further equip the models with direct and content-based controls on innate music languages such as pitch, chords and drum track. To this end, we contribute Coco-Mulla, a content-based control method for music large language modeling. It uses a parameter-efficient fine-tuning (PEFT) method tailored for Transformer-based audio models. Experiments show that our approach achieved high-quality music generation with low-resource semi-supervised learning, tuning with less than 4% parameters compared to the original model and training on a small dataset with fewer than 300 songs. Moreover, our approach enables effective content-based controls, and we illustrate the control power via chords and rhythms, two of the most salient features of music audio. Furthermore, we show that by combining content-based controls and text descriptions, our system achieves flexible music variation generation and arrangement. Our source codes and demos are available online.

cs.AI↗

arXiv2024

Arrange, Inpaint, and Refine: Steerable Long-term Music Audio Generation and Editing via Content-based Controls

Controllable music generation plays a vital role in human-AI music co-creation. While Large Language Models (LLMs) have shown promise in generating high-quality music, their focus on autoregressive generation limits their utility in music editing tasks. To address this gap, we propose a novel approach leveraging a parameter-efficient heterogeneous adapter combined with a masking training scheme. This approach enables autoregressive language models to seamlessly address music inpainting tasks. Additionally, our method integrates frame-level content-based controls, facilitating track-conditioned music refinement and score-conditioned music arrangement. We apply this method to fine-tune MusicGen, a leading autoregressive music generation model. Our experiments demonstrate promising results across multiple music editing tasks, offering more flexible controls for future AI-driven music editing tools. The source codes and a demo page showcasing our work are available at https://kikyo-16.github.io/AIR.

cs.SD↗

arXiv2024

Loop Copilot: Conducting AI Ensembles for Music Generation and Iterative Editing

Creating music is iterative, requiring varied methods at each stage. However, existing AI music systems fall short in orchestrating multiple subsystems for diverse needs. To address this gap, we introduce Loop Copilot, a novel system that enables users to generate and iteratively refine music through an interactive, multi-round dialogue interface. The system uses a large language model to interpret user intentions and select appropriate AI models for task execution. Each backend model is specialized for a specific task, and their outputs are aggregated to meet the user's requirements. To ensure musical coherence, essential attributes are maintained in a centralized table. We evaluate the effectiveness of the proposed system through semi-structured interviews and questionnaires, highlighting its utility not only in facilitating music creation but also its potential for broader applications.

cs.SD↗

arXiv2024

The Interpretation Gap in Text-to-Music Generation Models

Large-scale text-to-music generation models have significantly enhanced music creation capabilities, offering unprecedented creative freedom. However, their ability to collaborate effectively with human musicians remains limited. In this paper, we propose a framework to describe the musical interaction process, which includes expression, interpretation, and execution of controls. Following this framework, we argue that the primary gap between existing text-to-music models and musicians lies in the interpretation stage, where models lack the ability to interpret controls from musicians. We also propose two strategies to address this gap and call on the music information retrieval community to tackle the interpretation challenge to improve human-AI musical collaboration.

cs.SD↗

arXiv2024

On the boundedness of degenerate hypergraphs

We investigate the impact of a high-degree vertex in Turán problems for degenerate hypergraphs (including graphs). We say an $r$-graph $F$ is bounded if there exist constants $α, β>0$ such that for large $n$, every $n$-vertex $F$-free $r$-graph with a vertex of degree at least $α\binom{n-1}{r-1}$ has fewer than $(1-β) \cdot \mathrm{ex}(n,F)$ edges. The boundedness property is crucial for recent works~\cite{HHLLYZ23a,DHLY24} that aim to extend the classical Hajnal--Szemerédi Theorem and the anti-Ramsey theorems of Erdős--Simonovits--Sós. We show that many well-studied degenerate hypergraphs, such as all even cycles, most complete bipartite graphs, and the expansion of most complete bipartite graphs, are bounded. In addition, to prove the boundedness of the expansion of complete bipartite graphs, we introduce and solve a Zarankiewicz-type problem for $3$-graphs, strengthening a theorem by Kostochka--Mubayi--Verstraëte~\cite{KMV15}.

math.CO↗

arXiv2024

MusicMagus: Zero-Shot Text-to-Music Editing via Diffusion Models

Recent advances in text-to-music generation models have opened new avenues in musical creativity. However, music generation usually involves iterative refinements, and how to edit the generated music remains a significant challenge. This paper introduces a novel approach to the editing of music generated by such models, enabling the modification of specific attributes, such as genre, mood and instrument, while maintaining other aspects unchanged. Our method transforms text editing to \textit{latent space manipulation} while adding an extra constraint to enforce consistency. It seamlessly integrates with existing pretrained text-to-music diffusion models without requiring additional training. Experimental results demonstrate superior performance over both zero-shot and certain supervised baselines in style and timbre transfer evaluations. Additionally, we showcase the practical applicability of our approach in real-world music editing scenarios.

cs.SD↗

arXiv2024

Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomography

The advancement of artificial intelligence (AI) for organ segmentation and tumor detection is propelled by the growing availability of computed tomography (CT) datasets with detailed, per-voxel annotations. However, these AI models often struggle with flexibility for partially annotated datasets and extensibility for new classes due to limitations in the one-hot encoding, architectural design, and learning scheme. To overcome these limitations, we propose a universal, extensible framework enabling a single model, termed Universal Model, to deal with multiple public datasets and adapt to new classes (e.g., organs/tumors). Firstly, we introduce a novel language-driven parameter generator that leverages language embeddings from large language models, enriching semantic encoding compared with one-hot encoding. Secondly, the conventional output layers are replaced with lightweight, class-specific heads, allowing Universal Model to simultaneously segment 25 organs and six types of tumors and ease the addition of new classes. We train our Universal Model on 3,410 CT volumes assembled from 14 publicly available datasets and then test it on 6,173 CT volumes from four external datasets. Universal Model achieves first place on six CT tasks in the Medical Segmentation Decathlon (MSD) public leaderboard and leading performance on the Beyond The Cranial Vault (BTCV) dataset. In summary, Universal Model exhibits remarkable computational efficiency (6x faster than other dataset-specific models), demonstrates strong generalization across different hospitals, transfers well to numerous downstream tasks, and more importantly, facilitates the extensibility to new classes while alleviating the catastrophic forgetting of previously learned classes. Codes, models, and datasets are available at https://github.com/ljwztc/CLIP-Driven-Universal-Model

eess.IV↗

arXiv2024

Exploiting Structural Consistency of Chest Anatomy for Unsupervised Anomaly Detection in Radiography Images

Radiography imaging protocols focus on particular body regions, therefore producing images of great similarity and yielding recurrent anatomical structures across patients. Exploiting this structured information could potentially ease the detection of anomalies from radiography images. To this end, we propose a Simple Space-Aware Memory Matrix for In-painting and Detecting anomalies from radiography images (abbreviated as SimSID). We formulate anomaly detection as an image reconstruction task, consisting of a space-aware memory matrix and an in-painting block in the feature space. During the training, SimSID can taxonomize the ingrained anatomical structures into recurrent visual patterns, and in the inference, it can identify anomalies (unseen/modified visual patterns) from the test image. Our SimSID surpasses the state of the arts in unsupervised anomaly detection by +8.0%, +5.0%, and +9.9% AUC scores on ZhangLab, COVIDx, and CheXpert benchmark datasets, respectively. Code: https://github.com/MrGiovanni/SimSID

eess.IV↗

arXiv2023

Hypergraphs with infinitely many extremal constructions

We give the first exact and stability results for a hypergraph Turán problem with infinitely many extremal constructions that are far from each other in edit-distance. This includes an example of triple systems with Turán density $2/9$, thus answering some questions posed by the third and fourth authors and Reiher about the feasible region of hypergraphs. Our results also provide extremal constructions whose shadow density is a transcendental number. Our novel approach is to construct certain multilinear polynomials that attain their maximum (in the standard simplex) on a line segment and then to use these polynomials to define an operation on hypergraphs that gives extremal constructions.

math.CO↗

arXiv2023

Many vertex-disjoint even cycles of fixed length in a graph

For every integer $k \ge 3$, we determine the extremal structure of an $n$-vertex graph with at most $t$ vertex-disjoint copies of $C_{2k}$ when $n$ is sufficiently large and $t$ lies in the interval $\left[\frac{\mathrm{ex}(n,C_{2k})}{\varepsilon n}, \varepsilon n\right]$, where $\varepsilon>0$ is a constant depending only on $k$. The question for $k = 2$ and $t = o\left(\frac{\mathrm{ex}(n,C_{2k})}{n}\right)$ was explored in prior work~\cite{HHLLYZ23a}, revealing different extremal structures in these cases. Our result can be viewed as an extension of the theorems by Egawa~\cite{Ega96} and Verstraëte~\cite{Ver03}, where the focus was on the existence of many vertex-disjoint cycles of the same length without any length constraints.

math.CO↗