arXiv subjects

Yu Zhang

Publications and source records attributed to Yu Zhang.

At least 757 records · Page 42Linked to original sources

arXiv2024

Brain-Cognition Fingerprinting via Graph-GCCA with Contrastive Learning

Many longitudinal neuroimaging studies aim to improve the understanding of brain aging and diseases by studying the dynamic interactions between brain function and cognition. Doing so requires accurate encoding of their multidimensional relationship while accounting for individual variability over time. For this purpose, we propose an unsupervised learning model (called \underline{\textbf{Co}}ntrastive Learning-based \underline{\textbf{Gra}}ph Generalized \underline{\textbf{Ca}}nonical Correlation Analysis (CoGraCa)) that encodes their relationship via Graph Attention Networks and generalized Canonical Correlational Analysis. To create brain-cognition fingerprints reflecting unique neural and cognitive phenotype of each person, the model also relies on individualized and multimodal contrastive learning. We apply CoGraCa to longitudinal dataset of healthy individuals consisting of resting-state functional MRI and cognitive measures acquired at multiple visits for each participant. The generated fingerprints effectively capture significant individual differences and outperform current single-modal and CCA-based multimodal models in identifying sex and age. More importantly, our encoding provides interpretable interactions between those two modalities.

cs.CV↗

arXiv2024

Quantum-Assisted Joint Virtual Network Function Deployment and Maximum Flow Routing for Space Information Networks

Network function virtualization (NFV)-enabled space information network (SIN) has emerged as a promising method to facilitate global coverage and seamless service. This paper proposes a novel NFV-enabled SIN to provide end-to-end communication and computation services for ground users. Based on the multi-functional time expanded graph (MF-TEG), we jointly optimize the user association, virtual network function (VNF) deployment, and flow routing strategy (U-VNF-R) to maximize the total processed data received by users. The original problem is a mixed-integer linear program (MILP) that is intractable for classical computers. Inspired by quantum computing techniques, we propose a hybrid quantum-classical Benders' decomposition (HQCBD) algorithm. Specifically, we convert the master problem of the Benders' decomposition into the quadratic unconstrained binary optimization (QUBO) model and solve it with quantum computers. To further accelerate the optimization, we also design a multi-cut strategy based on the quantum advantages in parallel computing. Numerical results demonstrate the effectiveness and efficiency of the proposed algorithm and U-VNF-R scheme.

cs.NI↗

arXiv2024

LI-GS: Gaussian Splatting with LiDAR Incorporated for Accurate Large-Scale Reconstruction

Large-scale 3D reconstruction is critical in the field of robotics, and the potential of 3D Gaussian Splatting (3DGS) for achieving accurate object-level reconstruction has been demonstrated. However, ensuring geometric accuracy in outdoor and unbounded scenes remains a significant challenge. This study introduces LI-GS, a reconstruction system that incorporates LiDAR and Gaussian Splatting to enhance geometric accuracy in large-scale scenes. 2D Gaussain surfels are employed as the map representation to enhance surface alignment. Additionally, a novel modeling method is proposed to convert LiDAR point clouds to plane-constrained multimodal Gaussian Mixture Models (GMMs). The GMMs are utilized during both initialization and optimization stages to ensure sufficient and continuous supervision over the entire scene while mitigating the risk of over-fitting. Furthermore, GMMs are employed in mesh extraction to eliminate artifacts and improve the overall geometric quality. Experiments demonstrate that our method outperforms state-of-the-art methods in large-scale 3D reconstruction, achieving higher accuracy compared to both LiDAR-based methods and Gaussian-based methods with improvements of 52.6% and 68.7%, respectively.

cs.RO↗

arXiv2024

Efficient Low-Resolution Face Recognition via Bridge Distillation

Face recognition in the wild is now advancing towards light-weight models, fast inference speed and resolution-adapted capability. In this paper, we propose a bridge distillation approach to turn a complex face model pretrained on private high-resolution faces into a light-weight one for low-resolution face recognition. In our approach, such a cross-dataset resolution-adapted knowledge transfer problem is solved via two-step distillation. In the first step, we conduct cross-dataset distillation to transfer the prior knowledge from private high-resolution faces to public high-resolution faces and generate compact and discriminative features. In the second step, the resolution-adapted distillation is conducted to further transfer the prior knowledge to synthetic low-resolution faces via multi-task learning. By learning low-resolution face representations and mimicking the adapted high-resolution knowledge, a light-weight student model can be constructed with high efficiency and promising accuracy in recognizing low-resolution faces. Experimental results show that the student model performs impressively in recognizing low-resolution faces with only 0.21M parameters and 0.057MB memory. Meanwhile, its speed reaches up to 14,705, ~934 and 763 faces per second on GPU, CPU and mobile phone, respectively.

cs.CV↗

arXiv2024

Bitcoin Transaction Behavior Modeling Based on Balance Data

When analyzing Bitcoin users' balance distribution, we observed that it follows a log-normal pattern. Drawing parallels from the successful application of Gibrat's law of proportional growth in explaining city size and word frequency distributions, we tested whether the same principle could account for the log-normal distribution in Bitcoin balances. However, our calculations revealed that the exponent parameters in both the drift and variance terms deviate slightly from one. This suggests that Gibrat's proportional growth rule alone does not fully explain the log-normal distribution observed in Bitcoin users' balances. During our exploration, we discovered an intriguing phenomenon: Bitcoin users tend to fall into two distinct categories based on their behavior, which we refer to as ``poor" and ``wealthy" users. Poor users, who initially purchase only a small amount of Bitcoin, tend to buy more bitcoins first and then sell out all their holdings gradually over time. The certainty of selling all their coins is higher and higher with time. In contrast, wealthy users, who acquire a large amount of Bitcoin from the start, tend to sell off their holdings over time. The speed at which they sell their bitcoins is lower and lower over time and they will hold at least a small part of their initial holdings at last. Interestingly, the wealthier the user, the larger the proportion of their balance and the higher the certainty they tend to sell. This research provided an interesting perspective to explore bitcoin users' behaviors which may apply to other finance markets.

econ.GN↗

arXiv2024

Evaluating the Quality of Brain MRI Generators

Deep learning models generating structural brain MRIs have the potential to significantly accelerate discovery of neuroscience studies. However, their use has been limited in part by the way their quality is evaluated. Most evaluations of generative models focus on metrics originally designed for natural images (such as structural similarity index and Frechet inception distance). As we show in a comparison of 6 state-of-the-art generative models trained and tested on over 3000 MRIs, these metrics are sensitive to the experimental setup and inadequately assess how well brain MRIs capture macrostructural properties of brain regions (i.e., anatomical plausibility). This shortcoming of the metrics results in inconclusive findings even when qualitative differences between the outputs of models are evident. We therefore propose a framework for evaluating models generating brain MRIs, which requires uniform processing of the real MRIs, standardizing the implementation of the models, and automatically segmenting the MRIs generated by the models. The segmentations are used for quantifying the plausibility of anatomy displayed in the MRIs. To ensure meaningful quantification, it is crucial that the segmentations are highly reliable. Our framework rigorously checks this reliability, a step often overlooked by prior work. Only 3 of the 6 generative models produced MRIs, of which at least 95% had highly reliable segmentations. More importantly, the assessment of each model by our framework is in line with qualitative assessments, reinforcing the validity of our approach.

eess.IV↗

arXiv2024

Can millicharge be probed at future Super Tau Charm Facility via mono-$π^0$ searches?

We propose a new channel to search for millicharged particles at the future Super Tau Charm Facility (STCF) via mono-$π^0$ signature. For the first time, we compute the mono-$π^0$ signal events at the future STCF due to millicharged particle production, as well as due to standard model irreducible/reducible backgrounds. By utilizing the assumed 20 ab$^{-1}$ luminosity for each running energy with $\sqrt{s}= 2$ GeV, 4 GeV and 7 GeV, we derive the corresponding upper limits on millicharge, respectively. Via mono-$π^0$ searches at the future STCF with $\sqrt{s}=2$ GeV, the upper limits on millicharge can be improved than ArgoNeuT when the mass of millicharged particle is less than about 500 MeV, but are not very competitive compared to a latest derivation from a past BEBC experiment and a new SENSEI experiment. Regardless, the mono-$π^0$ searches could be an important complement to investigate the invisible particles, such as dark matter.

hep-ph↗

arXiv2024

Exploiting Deep Reinforcement Learning for Edge Caching in Cell-Free Massive MIMO Systems

Cell-free massive multiple-input-multiple-output is promising to meet the stringent quality-of-experience (QoE) requirements of railway wireless communications by coordinating many successional access points (APs) to serve the onboard users coherently. A key challenge is how to deliver the desired contents timely due to the radical changing propagation environment caused by the growing train speed. In this paper, we propose to proactively cache the likely-requesting contents at the upcoming APs which perform the coherent transmission to reduce end-to-end delay. A long-term QoE-maximization problem is formulated and two cache placement algorithms are proposed. One is based on heuristic convex optimization (HCO) and the other exploits deep reinforcement learning (DRL) with soft actor-critic (SAC). Compared to the conventional benchmark, numerical results show the advantage of our proposed algorithms on QoE and hit probability. With the advanced DRL model, SAC outperforms HCO on QoE by predicting the user requests accurately.

cs.IT↗

arXiv2024

Brain Network Diffusion-Driven fMRI Connectivity Augmentation for Enhanced Autism Spectrum Disorder Diagnosis

Functional magnetic resonance imaging (fMRI) is an emerging neuroimaging modality that is commonly modeled as networks of Regions of Interest (ROIs) and their connections, named functional connectivity, for understanding the brain functions and mental disorders. However, due to the high cost of fMRI data acquisition and labeling, the amount of fMRI data is usually small, which largely limits the performance of recognition models. With the rise of generative models, especially diffusion models, the ability to generate realistic samples close to the real data distribution has been widely used for data augmentations. In this work, we present a transformer-based latent diffusion model for functional connectivity generation and demonstrate the effectiveness of the diffusion model as an augmentation tool for fMRI functional connectivity. Furthermore, extended experiments are conducted to provide detailed analysis of the generation quality and interpretations for the learned feature pattern. Our code will be made public upon acceptance.

q-bio.NC↗

arXiv2024

Long-Range $ZZ$ Interaction via Resonator-Induced Phase in Superconducting Qubits

Superconducting quantum computing emerges as one of leading candidates for achieving quantum advantage. However, a prevailing challenge is the coding overhead due to limited quantum connectivity, constrained by nearest-neighbor coupling among superconducting qubits. Here, we propose a novel multimode coupling scheme using three resonators driven by two microwaves, based on the resonator-induced phase gate, to extend the $ZZ$ interaction distance between qubits. We demonstrate a CZ gate fidelity exceeding 99.9\% within 160 ns at free spectral range (FSR) of 1.4 GHz, and by optimizing driving pulses, we further reduce the residual photon to nearly $10^{-3}$ within 100 ns at FSR of 0.2 GHz. These facilitate the long-range CZ gate over separations reaching sub-meters, thus significantly enhancing qubit connectivity and making a practical step towards the scalable integration and modularization of quantum processors. Specifically, our approach supports the implementation of quantum error correction codes requiring high connectivity, such as low-density parity check codes that paves the way to achieving fault-tolerant quantum computing.

quant-ph↗

arXiv2024

Strong gravitational lensing in a Kerr black hole within Quantum Einstein Gravity

The detailed study of the strong gravitational lensing of a Kerr black hole within Quantum Einstein Gravity (QEG) is performed. We calculate the photon sphere, the deflection angle of light, and observables on the equatorial plane under the strong deflection limit in a vacuum. The presence of quantum effects reduces the radius of the photon sphere, the magnification, the position of relativistic images, and the time delays on the same side of the lens. However, it increases the strong deflection angle, the separations, and the time delays on the opposite side of the lens. By modeling M87* and Sgr A* as the Kerr black hole within QEG, we find that the time delays are more significant in M87*, while other observables are more pronounced in Sgr A*. Furthermore, we consider the influence of plasma on the gravitational lensing effect. Plasma causes an additional deflection of light, increasing the magnification, images position and the time delays, but decreasing the separations. More importantly, we calculate the time delays under the strong deflection limit in the presence of plasma, and they increase with higher plasma concentrations. Our research may help to evaluate the observational imprints left by such quantum effects in the propagation of light and the impact of plasma around black holes on gravitational lensing.

gr-qc↗

arXiv2024

Numerical Study of Flow Past a Wall-Mounted Dolphin Dorsal Fin at Low Reynolds Numbers

Dolphin swimming has been a captivating area of study, yet the hydrodynamics of the dorsal fin remain underexplored. In this study, we present three-dimensional simulations of flow around a wall-mounted dolphin dorsal fin, derived from a real dolphin scan. The NEK5000 (spectral element method) is employed with a second-order hex20 mesh to ensure high accuracy and computational efficiency in the simulations. A total of 13 cases were simulated, covering angles of attack (AoA) ranging from $0^\circ$ to $60^\circ$ and Reynolds numbers ($\text{Re}$) between 691 and 2000. Our results show that both drag and lift increase significantly with the AoA. Almost no vortex is observed at $\text{AoA} = 0^\circ$, whereas complex vortex structures emerge for $\text{AoA} \geq 30^\circ$, including half-horseshoe, hairpin, arch, and wake vortices. This study offers insights that could inform the design of next-generation underwater robots, heat exchangers, and submarine sails.

physics.flu-dyn↗

arXiv2024

From MOOC to MAIC: Reshaping Online Teaching and Learning through LLM-driven Agents

Since the first instances of online education, where courses were uploaded to accessible and shared online platforms, this form of scaling the dissemination of human knowledge to reach a broader audience has sparked extensive discussion and widespread adoption. Recognizing that personalized learning still holds significant potential for improvement, new AI technologies have been continuously integrated into this learning format, resulting in a variety of educational AI applications such as educational recommendation and intelligent tutoring. The emergence of intelligence in large language models (LLMs) has allowed for these educational enhancements to be built upon a unified foundational model, enabling deeper integration. In this context, we propose MAIC (Massive AI-empowered Course), a new form of online education that leverages LLM-driven multi-agent systems to construct an AI-augmented classroom, balancing scalability with adaptivity. Beyond exploring the conceptual framework and technical innovations, we conduct preliminary experiments at Tsinghua University, one of China's leading universities. Drawing from over 100,000 learning records of more than 500 students, we obtain a series of valuable observations and initial analyses. This project will continue to evolve, ultimately aiming to establish a comprehensive open platform that supports and unifies research, technology, and applications in exploring the possibilities of online education in the era of large model AI. We envision this platform as a collaborative hub, bringing together educators, researchers, and innovators to collectively explore the future of AI-driven online education.

cs.CY↗

arXiv2024

Spherical-like spectra for the description of the normal states of $^{108-120}$Cd in the SU3-IBM and the $Q_{2_{1}^{+}}$ anomaly

The Cd puzzle implies that the phonon excitation of a spherical nucleus should be questioned and refuted. For understanding this spherical-like $γ$-soft mode newly found experimentally, a possible answer was proposed recently in the SU3-IBM. In this paper, the evolutions of the normal states in $^{108-120}$Cd are investigated and compared with the experimental results. For better explaining the nearly zero B(E2) values between the $0_{2}^{+}$ state and the $2_{1}^{+}$ state, except for the SU(3) second-order and third-order Casimir operators, other SU(3) higher-order interactions are also considered in detail. It can be found that the results of theoretical fitting and experimental data agree well with simple parameter selection. The spherical-like spectra really exist. The deficiency may come from the lack of configuration mixing. The realistic spectra characteristics of the spherical-like spectra are found for $^{118,120}$Cd and the electric quadrupole moments of the $2_{1}^{+}$ state are predicted. The $Q_{2_{1}^{+}}$ anomaly in $^{108-116}$Cd are also discussed.

nucl-th↗

arXiv2024

Statistical and dynamical aspects of quantum chaos in a kicked Bose-Hubbard dimer

Systems of interacting bosons in double-well potentials, modeled by two-site Bose-Hubbard models, are of significant theoretical and experimental interest and attracted intensive studies in contexts ranging from many-body physics and quantum dynamics to the onset of quantum chaos. In this work we systematically study a kicked two-site Bose-Hubbard model (Bose-Hubbard dimer) with the on-site potential difference being periodically modulated. Our model can be equivalently represented as a kicked Lipkin-Meshkov-Glick model and thus displays different dynamical behaviors from the kicked top model. By analyzing spectral statistics of Floquet operator, we unveil that the system undergoes a transition from regularity to chaos with increasing the interaction strength. Then based on semiclassical approximation and the analysis of Rényi entropy of coherent states in the basis of Floquet operator eigenstates, we reveal the local chaotic features of our model, which indicate the existence of integrable islands even in the deep chaotic regime. The semiclassical analysis also suggests that the system in chaotic regime may display different dynamical behavior depending on the choice of initial states. Finally, we demonstrate that dynamical signatures of chaos can be manifested by studying dynamical evolution of local operators and out of time order correlation function as well as the entanglement entropy. Our numerical results exhibit the richness of dynamics of the kicked Bose-Hubbard dimer in both regular and chaotic regimes as the initial states are chosen as coherent spin states located in different locations of phase space.

quant-ph↗

arXiv2024

Monadring: A lightweight consensus protocol to offer Validation-as-a-Service to AVS nodes

Existing blockchain networks are often large-scale, requiring transactions to be synchronized across the entire network to reach consensus. On-chain computations can be prohibitively expensive, making many CPU-intensive computations infeasible. Inspired by the structure of IBM's token ring networks, we propose a lightweight consensus protocol called Monadring to address these issues. Monadring allows nodes within a large blockchain network to form smaller subnetworks, enabling faster and more cost-effective computations while maintaining the security guarantees of the main blockchain network. To further enhance Monadring's security, we introduce a node rotation mechanism based on Verifiable Random Function (VRF) and blind voting using Fully Homomorphic Encryption (FHE) within the smaller subnetwork. Unlike the common voting-based election of validator nodes, Monadring leverages FHE to conceal voting information, eliminating the advantage of the last mover in the voting process. This paper details the design and implementation of the Monadring protocol and evaluates its performance and feasibility through simulation experiments. Our research contributes to enhancing the practical utility of blockchain technology in large-scale application scenarios.

cs.DC↗

arXiv2024

Nemesis: Normalizing the Soft-prompt Vectors of Vision-Language Models

With the prevalence of large-scale pretrained vision-language models (VLMs), such as CLIP, soft-prompt tuning has become a popular method for adapting these models to various downstream tasks. However, few works delve into the inherent properties of learnable soft-prompt vectors, specifically the impact of their norms to the performance of VLMs. This motivates us to pose an unexplored research question: ``Do we need to normalize the soft prompts in VLMs?'' To fill this research gap, we first uncover a phenomenon, called the \textbf{Low-Norm Effect} by performing extensive corruption experiments, suggesting that reducing the norms of certain learned prompts occasionally enhances the performance of VLMs, while increasing them often degrades it. To harness this effect, we propose a novel method named \textbf{N}ormalizing th\textbf{e} soft-pro\textbf{m}pt v\textbf{e}ctors of vi\textbf{si}on-language model\textbf{s} (\textbf{Nemesis}) to normalize soft-prompt vectors in VLMs. To the best of our knowledge, our work is the first to systematically investigate the role of norms of soft-prompt vector in VLMs, offering valuable insights for future research in soft-prompt tuning. The code is available at \texttt{\href{https://github.com/ShyFoo/Nemesis}{https://github.com/ShyFoo/Nemesis}}.

cs.CV↗

arXiv2024

LMM-VQA: Advancing Video Quality Assessment with Large Multimodal Models

The explosive growth of videos on streaming media platforms has underscored the urgent need for effective video quality assessment (VQA) algorithms to monitor and perceptually optimize the quality of streaming videos. However, VQA remains an extremely challenging task due to the diverse video content and the complex spatial and temporal distortions, thus necessitating more advanced methods to address these issues. Nowadays, large multimodal models (LMMs), such as GPT-4V, have exhibited strong capabilities for various visual understanding tasks, motivating us to leverage the powerful multimodal representation ability of LMMs to solve the VQA task. Therefore, we propose the first Large Multi-Modal Video Quality Assessment (LMM-VQA) model, which introduces a novel spatiotemporal visual modeling strategy for quality-aware feature extraction. Specifically, we first reformulate the quality regression problem into a question and answering (Q&A) task and construct Q&A prompts for VQA instruction tuning. Then, we design a spatiotemporal vision encoder to extract spatial and temporal features to represent the quality characteristics of videos, which are subsequently mapped into the language space by the spatiotemporal projector for modality alignment. Finally, the aligned visual tokens and the quality-inquired text tokens are aggregated as inputs for the large language model (LLM) to generate the quality score and level. Extensive experiments demonstrate that LMM-VQA achieves state-of-the-art performance across five VQA benchmarks, exhibiting an average improvement of $5\%$ in generalization ability over existing methods. Furthermore, due to the advanced design of the spatiotemporal encoder and projector, LMM-VQA also performs exceptionally well on general video understanding tasks, further validating its effectiveness. Our code will be released at https://github.com/Sueqk/LMM-VQA.

cs.CV↗