arXiv subjects

Yu Tian

Publications and source records attributed to Yu Tian.

At least 181 records · Page 10Linked to original sources

arXiv2024

AI-generated Image Quality Assessment in Visual Communication

Assessing the quality of artificial intelligence-generated images (AIGIs) plays a crucial role in their application in real-world scenarios. However, traditional image quality assessment (IQA) algorithms primarily focus on low-level visual perception, while existing IQA works on AIGIs overemphasize the generated content itself, neglecting its effectiveness in real-world applications. To bridge this gap, we propose AIGI-VC, a quality assessment database for AI-Generated Images in Visual Communication, which studies the communicability of AIGIs in the advertising field from the perspectives of information clarity and emotional interaction. The dataset consists of 2,500 images spanning 14 advertisement topics and 8 emotion types. It provides coarse-grained human preference annotations and fine-grained preference descriptions, benchmarking the abilities of IQA methods in preference prediction, interpretation, and reasoning. We conduct an empirical study of existing representative IQA methods and large multi-modal models on the AIGI-VC dataset, uncovering their strengths and weaknesses.

cs.CV↗

arXiv2024

Multipartite Entanglement Routing as a Hypergraph Immersion Problem

Multipartite entanglement, linking multiple nodes simultaneously, is a higher-order correlation that offers advantages over pairwise connections in quantum networks (QNs). Creating reliable, large-scale multipartite entanglement requires entanglement routing, a process that combines local, short-distance connections into a long-distance connection, which can be considered as a transformation of network topology. Here, we address the question of whether a QN can be topologically transformed into another via entanglement routing. Our key result is an exact mapping from multipartite entanglement routing to Nash-Williams's graph immersion problem, extended to hypergraphs. This generalized hypergraph immersion problem introduces a partial order between QN topologies, permitting certain topological transformations while precluding others, offering discerning insights into the design and manipulation of higher-order network topologies in QNs.

quant-ph↗

arXiv2024

Dirac-Equation Signal Processing: Physics Boosts Topological Machine Learning

Topological signals are variables or features associated with both nodes and edges of a network. Recently, in the context of Topological Machine Learning, great attention has been devoted to signal processing of such topological signals. Most of the previous topological signal processing algorithms treat node and edge signals separately and work under the hypothesis that the true signal is smooth and/or well approximated by a harmonic eigenvector of the Hodge-Laplacian, which may be violated in practice. Here we propose Dirac-equation signal processing, a framework for efficiently reconstructing true signals on nodes and edges, also if they are not smooth or harmonic, by processing them jointly. The proposed physics-inspired algorithm is based on the spectral properties of the topological Dirac operator. It leverages the mathematical structure of the topological Dirac equation to boost the performance of the signal processing algorithm. We discuss how the relativistic dispersion relation obeyed by the topological Dirac equation can be used to assess the quality of the signal reconstruction. Finally, we demonstrate the improved performance of the algorithm with respect to previous algorithms. Specifically, we show that Dirac-equation signal processing can also be used efficiently if the true signal is a non-trivial linear combination of more than one eigenstate of the Dirac equation, as it generally occurs for real signals.

cond-mat.dis-nn↗

arXiv2024

Spatially Visual Perception for End-to-End Robotic Learning

Recent advances in imitation learning have shown significant promise for robotic control and embodied intelligence. However, achieving robust generalization across diverse mounted camera observations remains a critical challenge. In this paper, we introduce a video-based spatial perception framework that leverages 3D spatial representations to address environmental variability, with a focus on handling lighting changes. Our approach integrates a novel image augmentation technique, AugBlender, with a state-of-the-art monocular depth estimation model trained on internet-scale data. Together, these components form a cohesive system designed to enhance robustness and adaptability in dynamic scenarios. Our results demonstrate that our approach significantly boosts the success rate across diverse camera exposures, where previous models experience performance collapse. Our findings highlight the potential of video-based spatial perception models in advancing robustness for end-to-end robotic learning, paving the way for scalable, low-cost solutions in embodied intelligence.

cs.CV↗

arXiv2024

Finding hypergraph immersion is fixed-parameter tractable

Immersion minor is an important variant of graph minor, defined through an injective mapping from vertices in a smaller graph $H$ to vertices in a larger graph $G$ where adjacent elements of the former are connected in the latter by edge-disjoint paths. Here, we consider the immersion problem in the emerging field of hypergraphs. We first define hypergraph immersion by extending the injective mapping to hypergraphs. We then prove that finding a hypergraph immersion is fixed-parameter tractable, namely, there exists an $O(N^6)$ polynomial-time algorithm to determine whether a fixed hypergraph $H$ can be immersed in a hypergraph $G$ with $N$ vertices. Additionally, we present the dual hypergraph immersion problem and provide further characteristics of the algorithmic complexity.

cs.DM↗

arXiv2024

Dynamical behaviour of soliton train in holographic superfluids at zero temperature

We construct a soliton train configuration with holographic superfluid model under AdS soliton background. We investigate the stability of a soliton train using Bloch waves under two distinct quantization schemes. Upon imposing a minor perturbation on the soliton train system, it has been observed that there exist two elastic modes and one phonon mode. Most importantly, we find, under soliton train background, that there is a rich phase diagram concerning the chemical potential under standard quantization. Nevertheless, there are no unstable modes under alternative quantization.

hep-th↗

arXiv2024

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging

Deep learning has achieved remarkable success in image classification and segmentation tasks. However, fairness concerns persist, as models often exhibit biases that disproportionately affect demographic groups defined by sensitive attributes such as race, gender, or age. Existing bias-mitigation techniques, including Subgroup Re-balancing, Adversarial Training, and Domain Generalization, aim to balance accuracy across demographic groups, but often fail to simultaneously improve overall accuracy, group-specific accuracy, and fairness due to conflicts among these interdependent objectives. We propose the Fair Distillation (FairDi) method, a novel fairness approach that decomposes these objectives by leveraging biased ``teacher'' models, each optimized for a specific demographic group. These teacher models then guide the training of a unified ``student'' model, which distills their knowledge to maximize overall and group-specific accuracies, while minimizing inter-group disparities. Experiments on medical imaging datasets show that FairDi achieves significant gains in both overall and group-specific accuracy, along with improved fairness, compared to existing methods. FairDi is adaptable to various medical tasks, such as classification and segmentation, and provides an effective solution for equitable model performance.

cs.CV↗

arXiv2024

The effects of data gaps on ringdown signals with space-based joint observation

In space-based gravitational wave observatories such as Taiji, LISA, and TianQin, data gaps are inevitable due to mission design, implementation, and the long duration of observations. These data gaps degrade data quality and cause spectral leakage during Fourier transformations. Since ringdown signals are a key scientific objective for these observatories, it is crucial to assess the impact of data gaps on ringdown signal observations. This study employs LISA's science requirement of maintaining a duty cycle of at least 75% to evaluate the worst-case impact of data gaps, and uses massive black hole binary catalogs to assess the average effects. Our findings indicate that, on average, data gaps increase parameter estimation errors by approximately 2.1 times for the (2,2) mode and by about 1.6 times for the (3,3) mode. Joint observation is commonly employed to alleviate the impact of data gaps. Similarly, we have evaluated the effects of joint observation with two configurations, Taiji-LISA and Taiji-TianQin, which demonstrate notable mitigation of the effects of data gaps. This work provides a quantitative assessment of data gaps on ringdown signals and highlights the significance of joint observation.

gr-qc↗

arXiv2024

A minimal model of cognition based on oscillatory and current-based reinforcement processes

Building mathematical models of brains is difficult because of the sheer complexity of the problem. One potential starting point is through basal cognition, which gives abstract representation of a range of organisms without central nervous systems, including fungi, slime moulds and bacteria. We propose one such model, demonstrating how a combination of oscillatory and current-based reinforcement processes can be used to couple resources in an efficient manner, mimicking the way these organisms function. A key ingredient in our model, not found in previous basal cognition models, is that we explicitly model oscillations in the number of particles (i.e. the nutrients, chemical signals or similar, which make up the biological system) and the flow of these particles within the modelled organisms. Using this approach, we find that our model builds efficient solutions, provided the environmental oscillations are sufficiently out of phase. We further demonstrate that amplitude differences can promote efficient solutions and that the system is robust to frequency differences. In the context of these findings, we discuss connections between our model and basal cognition in biological systems and slime moulds, in particular, how oscillations might contribute to self-organised problem-solving by these organisms.

q-bio.NC↗

arXiv2024

Nonlinear Evolution of unstable Charged de Sitter Black Holes with Hyperboloidal Formalism

Based on the hyperboloidal framework, we research the dynamical process of charged de Sitter black holes scattered by a charged scalar field. From the linear perturbation analysis, with the coupling strength within a critical interval, the charged scalar field with a superradiance frequency can induce the instability of the system. To reveal the real-time dynamics of such an instability, the nonlinear numerical simulation is implemented. The results show that the scalar field grows exponentially in the early stages and drastically extracts the charge from the black hole due to the superradiance, analogous to the charged black hole in a closed system. Differently, after saturation, the scalar field can not coexist with the central black hole stably and dissipates beyond the cosmological horizon slowly, leaving behind a bald black hole.

gr-qc↗

arXiv2024

Matrix-weighted networks for modeling multidimensional dynamics

Networks are powerful tools for modeling interactions in complex systems. While traditional networks use scalar edge weights, many real-world systems involve multidimensional interactions. For example, in social networks, individuals often have multiple interconnected opinions that can affect different opinions of other individuals, which can be better characterized by matrices. We propose a novel, general framework for modeling such multidimensional interacting dynamics: matrix-weighted networks (MWNs). We present the mathematical foundations of MWNs and examine consensus dynamics and random walks within this context. Our results reveal that the coherence of MWNs gives rise to non-trivial steady states that generalize the notions of communities and structural balance in traditional networks.

cs.SI↗

arXiv2024

Dynamical evolution of spinodal decomposition in holographic superfluids

We study the nonlinear dynamical evolution of spinodal decomposition in a first-order superfluid phase transition using a simple holographic model in the probe limit. We first confirm the linear stability analysis based on quasinormal modes and verify the existence of a critical length scale related to a gradient instability -- negative speed of sound squared -- of the superfluid sound mode, which is a consequence of a negative thermodynamic charge susceptibility. We present a comparison between our case and the standard Cahn-Hilliard equation for spinodal instability, in which a critical length scale can be also derived based on a diffusive instability. We then perform several numerical tests which include the nonlinear time evolution directly from an unstable state and fast quenches from a stable to an unstable state in the spinodal region. Our numerical results provide a real time description of spinodal decomposition and phase separation in one and two spatial dimensions. We reveal the existence of four different stages in the dynamical evolution, and characterize their main properties. Finally, we investigate the strength of dynamical heterogeneity using the spatial variance of the local chemical potential and we correlate the latter to other features of the dynamical evolution.

hep-th↗

arXiv2024

Generalized Robot Learning Framework

Imitation based robot learning has recently gained significant attention in the robotics field due to its theoretical potential for transferability and generalizability. However, it remains notoriously costly, both in terms of hardware and data collection, and deploying it in real-world environments demands meticulous setup of robots and precise experimental conditions. In this paper, we present a low-cost robot learning framework that is both easily reproducible and transferable to various robots and environments. We demonstrate that deployable imitation learning can be successfully applied even to industrial-grade robots, not just expensive collaborative robotic arms. Furthermore, our results show that multi-task robot learning is achievable with simple network architectures and fewer demonstrations than previously thought necessary. As the current evaluating method is almost subjective when it comes to real-world manipulation tasks, we propose Voting Positive Rate (VPR) - a novel evaluation strategy that provides a more objective assessment of performance. We conduct an extensive comparison of success rates across various self-designed tasks to validate our approach. To foster collaboration and support the robot learning community, we have open-sourced all relevant datasets and model checkpoints, available at huggingface.co/ZhiChengAI.

cs.RO↗

arXiv2024

VMAS: Video-to-Music Generation via Semantic Alignment in Web Music Videos

We present a framework for learning to generate background music from video inputs. Unlike existing works that rely on symbolic musical annotations, which are limited in quantity and diversity, our method leverages large-scale web videos accompanied by background music. This enables our model to learn to generate realistic and diverse music. To accomplish this goal, we develop a generative video-music Transformer with a novel semantic video-music alignment scheme. Our model uses a joint autoregressive and contrastive learning objective, which encourages the generation of music aligned with high-level video content. We also introduce a novel video-beat alignment scheme to match the generated music beats with the low-level motions in the video. Lastly, to capture fine-grained visual cues in a video needed for realistic background music generation, we introduce a new temporal video encoder architecture, allowing us to efficiently process videos consisting of many densely sampled frames. We train our framework on our newly curated DISCO-MV dataset, consisting of 2.2M video-music samples, which is orders of magnitude larger than any prior datasets used for video music generation. Our method outperforms existing approaches on the DISCO-MV and MusicCaps datasets according to various music generation evaluation metrics, including human evaluation. Results are available at https://genjib.github.io/project_page/VMAs/index.html

cs.MM↗

arXiv2024

On the construction of ultra-light MDS matrices

In recent years, the Substitution-Permutation Network has emerged as a crucial structure for constructing symmetric key ciphers. Composed primarily of linear matrices and nonlinear S-boxes, it offers a robust foundation for cryptographic security. Among the various metrics used to assess the cryptographic properties of linear matrices, the branch number stands out as a particularly important index. Matrices with an optimal branch number are referred to as MDS matrices and are highly prized in the field of cryptography. In this paper we delve into the construction of lightweight MDS matrices. We commence implementation trees of MDS matrices, which is a vital tool for understanding and manipulating their implementations, and then present an algorithm that efficiently enumerates all the lightest MDS matrices based on the word representation. As results, we obtain a series of ultra-lightweight $4\times 4$ MDS matrices, remarkably, 4-bit input MDS matrices with 35 XOR operations and 8-bit input ones with 67 XOR operations . These matrices represent the most comprehensive lightweight MDS matrices available to date. Furthermore, we craft some involution $4\times 4$ MDS matrices with a mere 68 XOR gates.To our best knowledge, they are the best up to date. In the realm of higher-order MDS matrices, we have successfully constructed $5\times 5$ and $6\times 6$ matrices with 114 and 148 XOR gates respectively. These findings outperform the current state-of-the-art.

cs.CR↗

arXiv2024

Direct Readout of Nitrogen-Vacancy Hybrid-Spin Quantum Register in Diamond by Photon Arrival Time Analysis

Quantum state readout plays a pivotal role in quantum technologies, spanning applications in sensing, computation, and secure communication. In this work, we introduce a new approach for efficiently reading populations of hybrid-spin states in the nitrogen-vacancy center of diamond using a single laser pulse, which utilizes the excited state level anti-crossing mechanism at around 500 Gs. Reading spin state populations through this approach achieves the same outcome as traditional quantum state diagonal tomography but significantly reduces the experimental time by an order of magnitude while maintaining fidelity. Moreover, this approach may be extended to encompass full-state tomography, thereby obviating the requirement for a sequence of spin manipulations and mitigating errors induced by decoherence throughout the procedure.

quant-ph↗

arXiv2024

Wavepacket interference of two photons through a beam splitter: from temporal entanglement to wavepacket shaping

Quantum interferences based on beam splitting are widely used for entanglement. However, the quantitative measurement of the entanglement in terms of temporal modes and wavepacket shaping facilitated by this entanglement remain unexplored. Here we analytically study the interference of two photons with different temporal shapes through a beam splitter (BS), then propose its application in temporal entanglement and shaping of photons. The temporal entanglement described by Von Neumann entropy is determined by the splitting ratio of BS and temporal indistinguishability of input photons. We found that maximum mode entanglement can be achieved with a 50/50 BS configuration, enabling the generation of a Bell state encoded in temporal modes, independent of the exact form of the input photons. Then, detecting one of the entangled photons at a specific time enables the probabilistic shaping of the other photon. This process can shape the exponentially decaying (ED) wavepacket into the ED sine shapes, which can be further shaped into Gaussian shapes with fidelity exceeding 99\%. The temporal entanglement and shaping of photons based on interference may solve the shape mismatch issues in large-scale optical quantum networks.

quant-ph↗

arXiv2024

Near-symmetric multiport beam splitting for high-NOON state preparation on nonlocal metasurface

Polarization beam splitting (BS) has been implemented on gradient metasurface with local response for entanglement manipulation and state reconstruction. To realize more degrees of light modulation, nonlocal modes, manifested as wavelength and momentum selectivity, should be applied into metasurface BS. Here, we demonstrate that single nonlocal phase gradient metasurface(NPGM) can function as a series of independent near-symmetric multiport BS,constructed by its momentum-polarization mode subspaces.Then, using any of above BS with simultaneous multiphoton interference, high-photon NOON states are prepared with high success probability and fidelity. For example,four-mode four-photon NOON state is obtained with 34.8% success probability and fidelity of 99.9%, greatly higher than those previously reported.With unique capability of multiphoton interference, this multiport BS on single NPGM can be directly used in the on-chip quantum photonics. Also, the efficient generation of high-photon NOON states with above BS has potential applications in quantum precision measurement.

quant-ph↗