arXiv subjects

Yu Zhang

Publications and source records attributed to Yu Zhang.

At least 757 records · Page 42Linked to original sources

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↗

arXiv2024

Enhancing Sharpness-Aware Minimization by Learning Perturbation Radius

Sharpness-aware minimization (SAM) is to improve model generalization by searching for flat minima in the loss landscape. The SAM update consists of one step for computing the perturbation and the other for computing the update gradient. Within the two steps, the choice of the perturbation radius is crucial to the performance of SAM, but finding an appropriate perturbation radius is challenging. In this paper, we propose a bilevel optimization framework called LEarning the perTurbation radiuS (LETS) to learn the perturbation radius for sharpness-aware minimization algorithms. Specifically, in the proposed LETS method, the upper-level problem aims at seeking a good perturbation radius by minimizing the squared generalization gap between the training and validation losses, while the lower-level problem is the SAM optimization problem. Moreover, the LETS method can be combined with any variant of SAM. Experimental results on various architectures and benchmark datasets in computer vision and natural language processing demonstrate the effectiveness of the proposed LETS method in improving the performance of SAM.

cs.LG↗

arXiv2024

Saturation Numbers for Linear Forests $P_7+tP_2$

Let $H$ be a fixed graph, a graph G is $H$-saturated if it has no copy of $H$ in $G$, but the addition of any edge in $E(\overline G)$ to $G$ results in an $H$-subgraph. The saturation number sat$(n,H)$ is the minimum number of edges in an $H$-saturated graph on $n$ vertices. In this paper, we determine the saturation number sat$(n,P_7+tP_2)$ for $n\geq \frac {14}{5}t+27$ and characterize the extremal graphs for $n\geq \frac{14}{13}(3t+25)$.

math.CO↗

arXiv2024

Building Decision Making Models Through Language Model Regime

We propose a novel approach for decision making problems leveraging the generalization capabilities of large language models (LLMs). Traditional methods such as expert systems, planning algorithms, and reinforcement learning often exhibit limited generalization, typically requiring the training of new models for each unique task. In contrast, LLMs demonstrate remarkable success in generalizing across varied language tasks, inspiring a new strategy for training decision making models. Our approach, referred to as "Learning then Using" (LTU), entails a two-stage process. Initially, the \textit{learning} phase develops a robust foundational decision making model by integrating diverse knowledge from various domains and decision making contexts. The subsequent \textit{using} phase refines this foundation model for specific decision making scenarios. Distinct from other studies that employ LLMs for decision making through supervised learning, our LTU method embraces a versatile training methodology that combines broad pre-training with targeted fine-tuning. Experiments in e-commerce domains such as advertising and search optimization have shown that LTU approach outperforms traditional supervised learning regimes in decision making capabilities and generalization. The LTU approach is the first practical training architecture for both single-step and multi-step decision making tasks combined with LLMs, which can be applied beyond game and robot domains. It provides a robust and adaptable framework for decision making, enhances the effectiveness and flexibility of various systems in tackling various challenges.

cs.CL↗

arXiv2024

Enhanced Prediction of Multi-Agent Trajectories via Control Inference and State-Space Dynamics

In the field of autonomous systems, accurately predicting the trajectories of nearby vehicles and pedestrians is crucial for ensuring both safety and operational efficiency. This paper introduces a novel methodology for trajectory forecasting based on state-space dynamic system modeling, which endows agents with models that have tangible physical implications. To enhance the precision of state estimations within the dynamic system, the paper also presents a novel modeling technique for control variables. This technique utilizes a newly introduced model, termed "Mixed Mamba," to derive initial control states, thereby improving the predictive accuracy of these variables. Moverover, the proposed approach ingeniously integrates graph neural networks with state-space models, effectively capturing the complexities of multi-agent interactions. This combination provides a robust and scalable framework for forecasting multi-agent trajectories across a range of scenarios. Comprehensive evaluations demonstrate that this model outperforms several established benchmarks across various metrics and datasets, highlighting its significant potential to advance trajectory forecasting in autonomous systems.

cs.RO↗

arXiv2024

Shadow of Kerr black hole surrounded by a cloud of strings in Rastall gravity and constraints from M87*

Motivated by the first image of a black hole captured by the Event Horizon Telescope (EHT), there has been a surge of research using observations of black hole shadows to test theories of gravity. In this paper, we carry out a study related to the shadow of Kerr black holes surrounded by a cloud of strings in Rastall gravity, which deviates from the Kerr black hole due to the presence of the string parameter $a_0$ and the parameter $β$. The horizons, ergospheres, and photon region of the black hole are shown. Moreover, we explore the shadow and observations of the black hole, which are closely linked to the parameters $a_0$ and $β$. By treating \text{M87*} as a Kerr black hole surrounded by a cloud of strings under Rastall gravity, we constrain the black hole parameters using the EHT observations. For a given $β$, the circularity deviation of the black hole obeys $ΔC\lesssim0.1$ in all regions. The angular diameter $θ_{d}=42\pm3μas$ provides the upper bound of parameters $a$ and $a_0$ for fixed $β$. The shadow axis ratio satisfies the observation data of EHT ($1<D_x\lesssim4/3$) in the whole space for a given $β$. These results are consistent with the public information from EHT. In other words, candidates for real astrophysical black holes can be Kerr black holes surrounded by a cloud of strings in Rastall gravity.

gr-qc↗

arXiv2024

Quantum Lotka-Volterra dynamics

Physical systems that display competitive non-linear dynamics have played a key role in the development of mathematical models of Nature. Important examples include predator-prey models in ecology, biology, consumer-resource models in economics, and reaction-diffusion equations in chemical reactions. However, as real world systems are embedded in complex environments, where it is difficult or even impossible to control external parameters, quantitative comparison between measurements and simple models remains challenging. This motivates the search for competitive dynamics in isolated physical systems, with precise control. An ideal candidate is laser excitation in dilute atomic ensembles. For example, atoms in highly-excited Rydberg states display rich many-body dynamics including ergodicity breaking, synchronisation and time crystals. Here, we demonstrate predator-prey dynamics by laser excitation and ionisation of Rydberg atoms in a room temperature vapour cell. Ionisation of excited atoms produce electric fields that suppress further excitation. This starves the ionisation process of resource, giving rise to predator-prey dynamics. By comparing our results to the Lotka-Volterra model, we demonstrate that as well applications in non-linear dynamics, our experiment has applications in metrology, and remote sensing of localised plasmas.

quant-ph↗

arXiv2024

Quasinormal modes, Hawking radiation and absorption of the massless scalar field for Bardeen black hole surrounded by perfect fluid dark matter

We study the quasinormal modes, Hawking radiation and absorption cross-section of the Bardeen black hole surrounded by perfect fluid dark matter for a massless scalar field. Our results show that the oscillation frequency of quasinormal modes is enhanced as magnetic charge $g$ or the dark matter parameter $α$ increases. For damping rate of quasinormal modes, the influence of them is different. Specifically, the increase of dark matter parameter $α$ makes the damping rate increasing at first and then decreasing. While the damping rate is continuously decreasing with the increase of the magnetic charge $g$. Moreover, we find that the increase of the dark matter parameter $α$ enhances the power emission spectrum whereas magnetic charge $g$ suppresses it. This means that the lifespan of black holes increases for smaller value of $α$ and larger value of $g$ when other parameters are fixed. Finally, the absorption cross-section of the considered black hole is calculated with the help of the partial wave approach. Our results suggest that the absorption cross-section decreases with the dark matter parameter $α$ or the magnetic charge $g$ increasing.

physics.gen-ph↗

arXiv2024

Lite-Mind: Towards Efficient and Robust Brain Representation Network

The limited data availability and the low signal-to-noise ratio of fMRI signals lead to the challenging task of fMRI-to-image retrieval. State-of-the-art MindEye remarkably improves fMRI-to-image retrieval performance by leveraging a large model, i.e., a 996M MLP Backbone per subject, to align fMRI embeddings to the final hidden layer of CLIP's Vision Transformer (ViT). However, significant individual variations exist among subjects, even under identical experimental setups, mandating the training of large subject-specific models. The substantial parameters pose significant challenges in deploying fMRI decoding on practical devices. To this end, we propose Lite-Mind, a lightweight, efficient, and robust brain representation learning paradigm based on Discrete Fourier Transform (DFT), which efficiently aligns fMRI voxels to fine-grained information of CLIP. We elaborately design a DFT backbone with Spectrum Compression and Frequency Projector modules to learn informative and robust voxel embeddings. Our experiments demonstrate that Lite-Mind achieves an impressive 94.6% fMRI-to-image retrieval accuracy on the NSD dataset for Subject 1, with 98.7% fewer parameters than MindEye. Lite-Mind is also proven to be able to be migrated to smaller fMRI datasets and establishes a new state-of-the-art for zero-shot classification on the GOD dataset.

cs.CV↗

arXiv2024

Learning Variable Impedance Skills from Demonstrations with Passivity Guarantee

Robots are increasingly being deployed not only in workplaces but also in households. Effectively execute of manipulation tasks by robots relies on variable impedance control with contact forces. Furthermore, robots should possess adaptive capabilities to handle the considerable variations exhibited by different robotic tasks in dynamic environments, which can be obtained through human demonstrations. This paper presents a learning-from-demonstration framework that integrates force sensing and motion information to facilitate variable impedance control. The proposed approach involves the estimation of full stiffness matrices from human demonstrations, which are then combined with sensed forces and motion information to create a model using the non-parametric method. This model allows the robot to replicate the demonstrated task while also responding appropriately to new task conditions through the use of the state-dependent stiffness profile. Additionally, a novel tank based variable impedance control approach is proposed to ensure passivity by using the learned stiffness. The proposed approach was evaluated using two virtual variable stiffness systems. The first evaluation demonstrates that the stiffness estimated approach exhibits superior robustness compared to traditional methods when tested on manual datasets, and the second evaluation illustrates that the novel tank based approach is more easily implementable compared to traditional variable impedance control approaches.

cs.RO↗