arXiv subjects

Jin Wang

Publications and source records attributed to Jin Wang.

At least 199 records · Page 11Linked to original sources

arXiv2024

Dual forms of the orthogonality relations of some classical q-orthogonal polynomials

In this paper, by introducing new matrix operations and using a specific inverse relation, we establish the dual forms of the orthogonality relations for some well-known discrete and continuous $q$-orthogonal polynomials from the Askey-scheme such as the little and big $q$-Jacobi, $q$-Racah, (generalized) $q$-Laguerre, as well as the Askey-Wilson polynomials. As one of the most interesting results, we show that the Askey-Wilson $q$-beta integral represented in terms of the VWP-balanced $\,_8ϕ_7$ series is just a dual form of the orthogonality relation of the Askey-Wilson polynomials.

math.CO↗

arXiv2024

Property-preserving numerical approximation of a Cahn-Hilliard-Navier-Stokes model with variable density and degenerate mobility

In this paper, we present a new computational framework to approximate a Cahn-Hilliard-Navier-Stokes model with variable density and degenerate mobility that preserves the mass of the mixture, the pointwise bounds of the density and the decreasing energy. This numerical scheme is based on a finite element approximation for the Navier-Stokes fluid flow with discontinuous pressure and an upwind discontinuous Galerkin scheme for the Cahn-Hilliard part. Finally, several numerical experiments such as a convergence test and some well-known benchmark problems are conducted.

math.NA↗

arXiv2024

Energy Consumption Optimization, Response Time Differences and Indicators in Cortical Working Memory Revealed by Nonequilibrium

The neocortex, a complex system driving multi-region interactions, remains a core puzzle in neuroscience. Despite quantitative insights across brain scales, understanding the mechanisms underlying neural activities is challenging. Advances from Hopfield networks to large-scale cortical models have deepened neural network theory, yet these models often fall short of capturing global brain functions. In large-scale cortical networks, an intriguing hierarchy of timescales reflects diverse information processing speeds across spatial regions. As a non-equilibrium system, the brain incurs significant energy costs, with long-distance connectivity suggesting an evolutionary spatial organization. To explore these complexities, we introduce a nonequilibrium landscape flux approach to analyze cortical networks. This allows us to quantify potential landscapes and principal transition paths, uncovering dynamical characteristics across timescales. We examine whether temporal hierarchies correlate with stimuli distribution and how hierarchical networks exhibit differential responses. Furthermore, our analysis quantifies the thermodynamic cost of sustaining cognition, highlighting a link to network connectivity. These findings provide insights into energy consumption during cognitive processes and emphasize the spatial benefits for working memory tasks. Experimental validation is challenging due to evolutionary variability, making our theoretical approach valuable for quantifying complex dynamics. By assessing time irreversibility and critical slowdown, we gain predictive insights into network bifurcations and state transitions, offering practical tools for identifying cortical state changes. These results advance our understanding of cortical dynamics.

q-bio.NC↗

arXiv2024

WTDUN: Wavelet Tree-Structured Sampling and Deep Unfolding Network for Image Compressed Sensing

Deep unfolding networks have gained increasing attention in the field of compressed sensing (CS) owing to their theoretical interpretability and superior reconstruction performance. However, most existing deep unfolding methods often face the following issues: 1) they learn directly from single-channel images, leading to a simple feature representation that does not fully capture complex features; and 2) they treat various image components uniformly, ignoring the characteristics of different components. To address these issues, we propose a novel wavelet-domain deep unfolding framework named WTDUN, which operates directly on the multi-scale wavelet subbands. Our method utilizes the intrinsic sparsity and multi-scale structure of wavelet coefficients to achieve a tree-structured sampling and reconstruction, effectively capturing and highlighting the most important features within images. Specifically, the design of tree-structured reconstruction aims to capture the inter-dependencies among the multi-scale subbands, enabling the identification of both fine and coarse features, which can lead to a marked improvement in reconstruction quality. Furthermore, a wavelet domain adaptive sampling method is proposed to greatly improve the sampling capability, which is realized by assigning measurements to each wavelet subband based on its importance. Unlike pure deep learning methods that treat all components uniformly, our method introduces a targeted focus on important subbands, considering their energy and sparsity. This targeted strategy lets us capture key information more efficiently while discarding less important information, resulting in a more effective and detailed reconstruction. Extensive experimental results on various datasets validate the superior performance of our proposed method.

eess.IV↗

arXiv2024

Rabi oscillation and fractional population via the bound states in the continuum in a giant atom waveguide QED setup

We study the dynamics of two giant atoms interacting with a coupled resonator waveguide (CRW) beyond the Markovian approximation. The distinct atomic configurations determine the number of bound states in the continuum (BIC), leading to different dynamical behaviors. Our results show that when the system supports two BICs, Rabi oscillations dominate the dynamics, whereas fractional population dynamics emerge in the presence of a single BIC. The connection between these dynamics and the existence of BICs is further verified by analyzing the photonic distribution in the CRW during time evolution. These findings challenge the conventional notion that the environment always induces dissipation and decoherence. Instead, the bound states in the CRW-emitters coupled system can suppress complete dissipation of the emitters. This work offers an effective approach for controlling dissipative dynamics in open quantum systems.

quant-ph↗

arXiv2024

Highly stable modular-assembled laser system for a dual-atom-interferometer gyroscope

Operating atom-interferometer gyroscopes outside a laboratory environment is challenging primarily owing to the instability of laser systems. To enhance the thermal stability of free-space laser systems, a compact laser system using fiber lasers and all-quartz-jointed optical modules was developed for a dual-atom-interferometer gyroscope. Millimeter-scale optical elements jointed on quartz plates with identical quartz supports, ensure laser power stability and facilitate component upgrades. The primary diode laser was locked to the modulation transfer spectrum of Rb atoms, and Raman lasers were phase-locked to the primary laser. Frequencies for repumping, blow-away, and detection lasers were adjusted with acousto-optic modulators. At room temperature, laser power fluctuation was under 1:1000, polarization extinction ratio exceeded 30 dB, frequency fluctuation was below 91 kHz, and phase noise reached to -100 dBc/Hz @ 1 kHz. The optical modules were tested at 5--50 $^{\circ}$C and applied to a dual-atom-interferometer gyroscope. The fringe contrast was tested over the temperature range. The proposed system paves the way for promoting field applications of atom-interferometer sensors.

physics.atom-ph↗

arXiv2024

A fiber array architecture for atom quantum computing

Arrays of single atoms trapped in optical tweezers are increasingly recognized as a promising platform for scalable quantum computing. In both the fault-tolerant and NISQ eras, the ability to individually control qubits is essential for the efficient execution of quantum circuits. Time-division multiplexed control schemes based on atom shuttling or beam scanning have been employed to build programmable neutral atom quantum processors, but achieving high-rate, highly parallel gate operations remains a challenge. Here, we propose a fiber array architecture for atom quantum computing capable of fully independent control of individual atoms. The trapping and addressing lasers for each individual atom are emitted from the same optical waveguide, enabling robust control through common-mode suppression of beam pointing noise. Using a fiber array, we experimentally demonstrate the trapping and independent control of ten single atoms in two-dimensional optical tweezers, achieving individually addressed single-qubit gate with an average fidelity of 0.9966(3). Moreover, we perform simultaneous arbitrary single-qubit gate on four randomly selected qubits, resulting in an average fidelity of 0.9961(4). Our work paves the way for time-efficient execution of quantum algorithms on neutral atom quantum computers.

quant-ph↗

arXiv2024

Mechanism of the Nonequilibrium Phase Transition in Self-Propelled Particles with Alignment

Self-propelled particles with alignment, displaying ordered collective motions such as swarming, can be investigated by the well-known Vicsek model. However, challenges still remain regarding the nature of the associated phase transition. Here, we use the landscape-flux approach combined with the coarse-grained mapping method to reveal the underlying mechanism of the continuous or discontinuous order-disorder nonequilibrium phase transition in Vicsek model systems featuring diverse noise characteristics. It is found that the nonequilibrium flux inside the landscape in the density-alignment degree phase space always rotates counterclockwise, and tends to delocalize or destabilize the point attractor states, providing the dynamical driving force for altering the landscape shape and the system state. Furthermore, the variations in the averaged flux and entropy production rate exhibit pronounced differences across various noise types. This not only helps to reveal the dynamical and thermodynamical mechanisms of the order-disorder transition but also offers a useful tool to recognize the continuity of the transition. Our findings present a novel perspective for exploring nonequilibrium phase transition behaviors and other collective motions in various complex systems.

cond-mat.stat-mech↗

arXiv2024

Constraining the spin-gravity coupling effects to the $10^{-10}$-level with dual-species atom interferometers

Spin is one fundamental property of microscopic particles. A lot of theoretical work has postulated the possible coupling between spin and gravitation, which could result in the violation of equivalence principle. In our recent joint mass-and-energy test of the weak equivalence principle with a 10-meter $^{85}$Rb-$^{87}$Rb dual-species atom interferometer, the E${\rm \ddot{o}}$tv${\rm \ddot{o}}$s parameters of four $^{85}$Rb-$^{87}$Rb combinations with specific atomic spin states were measured to the $10^{-10}$-level (\textit{L. Zhou et al., Phys. Rev. A 104, 022822}). Here these experimental results are used to constrain the postulated spin-gravity coupling effects. The bounds on the spin-independent and spin-dependent anomalous passive gravitational mass tensors in L${\rm \ddot{a}}$mmerzahl's model are set to the $10^{-10}$-level, which improves existing bounds by three orders of magnitude. The constraints to the spin-independent electron- and proton-gravity coupling parameters in the gravitational standard-model extension are set to the $10^{-6}\, {\rm GeV}$-level.

physics.atom-ph↗

arXiv2024

Wrong-of-Thought: An Integrated Reasoning Framework with Multi-Perspective Verification and Wrong Information

Chain-of-Thought (CoT) has become a vital technique for enhancing the performance of Large Language Models (LLMs), attracting increasing attention from researchers. One stream of approaches focuses on the iterative enhancement of LLMs by continuously verifying and refining their reasoning outputs for desired quality. Despite its impressive results, this paradigm faces two critical issues: (1) Simple verification methods: The current paradigm relies solely on a single verification method. (2) Wrong Information Ignorance: Traditional paradigms directly ignore wrong information during reasoning and refine the logic paths from scratch each time. To address these challenges, we propose Wrong-of-Thought (WoT), which includes two core modules: (1) Multi-Perspective Verification: A multi-perspective verification method for accurately refining the reasoning process and result, and (2) Wrong Information Utilization: Utilizing wrong information to alert LLMs and reduce the probability of LLMs making same mistakes. Experiments on 8 popular datasets and 5 LLMs demonstrate that WoT surpasses all previous baselines. In addition, WoT exhibits powerful capabilities in difficult computation tasks.

cs.CL↗

arXiv2024

RoboNurse-VLA: Robotic Scrub Nurse System based on Vision-Language-Action Model

In modern healthcare, the demand for autonomous robotic assistants has grown significantly, particularly in the operating room, where surgical tasks require precision and reliability. Robotic scrub nurses have emerged as a promising solution to improve efficiency and reduce human error during surgery. However, challenges remain in terms of accurately grasping and handing over surgical instruments, especially when dealing with complex or difficult objects in dynamic environments. In this work, we introduce a novel robotic scrub nurse system, RoboNurse-VLA, built on a Vision-Language-Action (VLA) model by integrating the Segment Anything Model 2 (SAM 2) and the Llama 2 language model. The proposed RoboNurse-VLA system enables highly precise grasping and handover of surgical instruments in real-time based on voice commands from the surgeon. Leveraging state-of-the-art vision and language models, the system can address key challenges for object detection, pose optimization, and the handling of complex and difficult-to-grasp instruments. Through extensive evaluations, RoboNurse-VLA demonstrates superior performance compared to existing models, achieving high success rates in surgical instrument handovers, even with unseen tools and challenging items. This work presents a significant step forward in autonomous surgical assistance, showcasing the potential of integrating VLA models for real-world medical applications. More details can be found at https://robonurse-vla.github.io.

cs.RO↗

arXiv2024

Thermodynamics and kinetics of state switching for the asymptotically flat black hole in a cavity

We propose that the thermodynamics and the kinetics of state switching for the asymptotically flat black hole enclosed by a cavity can be studied in terms of the free energy landscape formalism. The generalized free energy for the black hole enclosed by a cavity in the canonical ensemble is derived by using the York's approach, where the temperature on the cavity and the charges inside the cavity are kept as the fixed parameters. By quantifying the corresponding free energy landscape, we obtain the phase diagrams for the black hole in cavity, which reveal a Hawking-Page type transition for the uncharged black hole and a Van der Waals type transition for the charged black hole. We further assume that the dynamics of black hole state switching is mutually determined by the gradient force and the stochastic force arising from the free energy landscape and the thermal noises respectively. We then derive a recurrence relation for the $n$-momentum of the first passage time distribution function, which enables the calculation of the kinetic times characterized by the mean first passage time and its relative fluctuation. Our analysis illustrates that the kinetics of black hole state switching is determined by the ensemble temperature and the barrier height on the free energy landscape.

gr-qc↗

arXiv2024

Memory in the Burst Occurrence of Repeating FRBs

Understanding the nature of repeating fast radio bursts (FRBs) is crucial to probe their underlying physics. In this work, we analyze the waiting time statistics between bursts of three repeating FRBs from four datasets. We find a universally pronounced dependency of the waiting times on the previous time interval (denoted as $λ_0$). We observe a temporal clustering where short waiting times tend to be followed by short ones, and long by long comparative to their mean value. This memory dependency is manifested in the conditional mean waiting time as well as in the conditional mean residual time to the next burst, both of which increase in direct proportion to $λ_0$. Consequently, the likelihood of experiencing a subsequent FRB burst within a given time window after the preceding burst is generally influenced by the burst history. We reveal that for the first time, these memory effects are present in the scale-invariant preconditioned waiting time distribution. We show that the memory effect provides a unified description of waiting times which may account for both the repeating FRBs and the apparent non-repeating FRBs (i.e. only observed one time). These results shed new light on the mechanism of FRBs.

astro-ph.HE↗

arXiv2024

Realization of cold atom gyroscope in space

High-precision gyroscopes in space are essential for fundamental physics research and navigation. Due to its potential high precision, the cold atom gyroscope is expected to be the next generation of gyroscopes in space. Here, we report the first realization of a cold atom gyroscope, which was demonstrated by the atom interferometer installed in the China Space Station (CSS) as a payload. By compensating for CSS's high dynamic rotation rate using a built-in piezoelectric mirror, spatial interference fringes in the interferometer are successfully obtained. Then, the optimized ratio of the Raman laser's angles is derived, the coefficients of the piezoelectric mirror are self-calibrated in orbit, and various systemic effects are corrected. We achieve a rotation measurement resolution of 50*10^-6 rad/s for a single shot and 17*10^-6 rad/s for an average number of 32. The measured rotation is (-1142+/-29)*10^-6 rad/s and is compatible with that recorded by the classical gyroscope of the CSS. This study paves the way for developing high-precision cold atom gyroscopes in space.

physics.atom-ph↗

arXiv2024

Towards a dissipative quantum classifier

In this paper, we propose a novel quantum classifier utilizing dissipative engineering. Unlike standard quantum circuit models, the classifier consists of a central spin-qubit model. By subjecting the auxiliary qubits to carefully tailored strong dissipations, we establish a one-to-one mapping between classical data and dissipative modes. This mapping enables the encoding of classical data within a decoherence-free subspace, where the central qubit undergoes evolution. The dynamics of the central qubit are governed by an effective Lindblad master equation, resulting in relaxation towards a steady state. We first demonstrate the capability of our model to prepare arbitrary single-qubit states by training the inter-coupling of the system and the external dissipations. By elucidating the underlying classification rule, we subsequently derive a quantum classifier. Leveraging a training set with labeled data, we train the dissipative central spin-qubit system to perform specific classification tasks akin to classical neural networks. Our study illuminates the untapped potential of the dissipative system for efficient and effective classification tasks in the realm of quantum machine learning.

quant-ph↗

arXiv2024

Transit time of black holes on generalized free energy landscape

Recently, the thermodynamics and kinetics of black hole phase transitions have garnered attention, particularly with the black hole radius being utilized as an order parameter within a generalized free energy landscape. In this framework, the local minima and maxima of the free energy correspond to stable and unstable states, respectively, while other states on the free energy landscape represent fluctuating black holes. Thermal fluctuations enable transitions between these stable states, and this stochastic kinetic behavior can be effectively described by the probabilistic Fokker-Planck equation.The transit time, defined as the time required for transitions or jumps between states, is a crucial physical quantity in phase transition kinetics, as it helps characterize the switching dynamics. By employing a harmonic transition state approximation, we examine the Hawking-Page phase transition and the Reissner-Nordström-Anti-de Sitter (RNAdS) black hole phase transition within the context of the generalized free energy landscape. We analytically quantify the transit time and its probability distribution for actual transition events, revealing the relationship between the mean transit time and the prefactor of the classical mean first passage time (MFPT). As the mean transit time decreases, the probability distribution narrows, indicating reduced fluctuations in transit time. These phenomena are connected to the topological structure of the generalized free energy landscape, the curvature of the free energy in both stable and unstable states, and the height of the energy barrier. We conclude that, for black hole phase transitions, the transit time serves as a characteristic timescale that reflects the actual jump time between states or to a transition state. Moreover, its relationship with the prefactor allows for a more precise quantification of the MFPT.

gr-qc↗

arXiv2024

Physics-informed neural networks incorporating energy dissipation for the phase-field model of ferroelectric microstructure evolution

Physics-informed neural networks (PINNs) are an emerging technique to solve partial differential equations (PDEs). In this work, we propose a simple but effective PINN approach for the phase-field model of ferroelectric microstructure evolution. This model is a time-dependent, nonlinear, and high-order PDE system of multi-physics, challenging to be solved using a baseline PINN. Considering that the acquisition of steady microstructures is one of the primary focuses in simulations of ferroelectric microstructure evolution, we simplify the time-dependent PDE system to be a static problem. This static problem, however, is ill-posed. To overcome this issue, a term originated from the law of energy dissipation is embedded into the loss function as an extra constraint for the PINN. With this modification, the PINN successfully predicts the steady ferroelectric microstructure without tracking the evolution process. In addition, although the proposed PINN approach cannot tackle the dynamic problem in a straightforward fashion, it is of benefit to the PINN prediction of the evolution process by providing labeled data. These data are crucial because they help the PINN avoid the propagation failure, a common failure mode of PINNs when predicting dynamic behaviors. The above mentioned advantages of the proposed PINN approach are demonstrated through a number of examples.

cond-mat.mtrl-sci↗

arXiv2024

Grounding Language Models in Autonomous Loco-manipulation Tasks

Humanoid robots with behavioral autonomy have consistently been regarded as ideal collaborators in our daily lives and promising representations of embodied intelligence. Compared to fixed-based robotic arms, humanoid robots offer a larger operational space while significantly increasing the difficulty of control and planning. Despite the rapid progress towards general-purpose humanoid robots, most studies remain focused on locomotion ability with few investigations into whole-body coordination and tasks planning, thus limiting the potential to demonstrate long-horizon tasks involving both mobility and manipulation under open-ended verbal instructions. In this work, we propose a novel framework that learns, selects, and plans behaviors based on tasks in different scenarios. We combine reinforcement learning (RL) with whole-body optimization to generate robot motions and store them into a motion library. We further leverage the planning and reasoning features of the large language model (LLM), constructing a hierarchical task graph that comprises a series of motion primitives to bridge lower-level execution with higher-level planning. Experiments in simulation and real-world using the CENTAURO robot show that the language model based planner can efficiently adapt to new loco-manipulation tasks, demonstrating high autonomy from free-text commands in unstructured scenes.

cs.RO↗