arXiv subjects

Wei Zheng

Publications and source records attributed to Wei Zheng.

At least 55 records · Page 3Linked to original sources

arXiv2024

Emergent Universal Quench Dynamics in Randomly Interacting Spin Models

Universality often emerges in low-energy equilibrium physics of quantum many-body systems, despite their microscopic complexity and variety. Recently, there has been a growing interest in studying far-from-equilibrium dynamics of quantum many-body systems. Such dynamics usually involves highly excited states beyond the traditional low-energy theory description. Whether universal behaviors can also emerge in such non-equilibrium dynamics is a central issue at the frontier of quantum dynamics. Here we report the experimental observation of universal dynamics by monitoring the spin depolarization process in a solid-state NMR system described by an ensemble of randomly interacting spins. The spin depolarization can be related to temporal spin-spin correlation functions at high temperatures. We discover a remarkable phenomenon that these correlation functions obey a universal functional form. This experimental fact helps us identify the dominant interacting processes in the spin depolarization dynamics that lead to this universality. Our observation demonstrates the existence of universality even in non-equilibrium dynamics at high temperatures, thereby complementing the well-established universality in low-energy physics.

cond-mat.str-el↗

arXiv2024

Highly sensitive AuNCs@GSH/Ch-PtNPs metal nanoprobes for fluorescent and colorimetric dual-mode detection of ascorbic acid in drink

Fluorescence detection is a commonly used analytical method with the advantages of fast response, good selectivity and low destructiveness. However, fluorescence detection, a single-mode detection method, has some limitations, such as background interference that affects the accuracy of the fluorescence signal, lack of visualization of the detection results, and low sensitivity for detecting low-concentration samples. In order to overcome the shortcomings of fluorescence single-mode detection, we used the dual-mode method of fluorescence and colorimetry to detect ascorbic acid. The dual-mode detection of AA by fluorescence and colorimetry in the probe system enhances the specificity and accuracy of the detection. This bimodal detection method solved the problem of low detection sensitivity in the low concentration range of the analytes to be tested, and was linear in the lower (0-50 μM) and higher (50-350 μM) concentration ranges, respectively, and had a lower detection limit (0.034 μM). This glutathione-based gold cluster assay is characterized by simplicity, rapidity and accuracy, and provides a new way for the quantitative analysis of ascorbic acid. In addition, the method was validated during the determination of AA in beverages, which has the advantages of high sensitivity and fast response time.

physics.optics↗

arXiv2024

Topological Origin of Floquet Thermalization in Periodically Driven Many-body Systems

Floquet engineering is a powerful manipulation method in modern quantum technology. However, unwanted heating is the main challenge of Floquet engineering, therefore the Floquet thermalization has attracting considerable attentions recently. In this work, we investigate thermalization of periodically driven many-body systems through the lens of Krylov complexity, and find a topological origin of different thermalization behaviors. We demonstrate that If the topology of the Krylov chain is nontrivial, a periodically driven system will reach a state with finite temperature. When the Krylov chain is topologically trivial, the system will be heated to infinite temperature. We further show that the prethermalization can be understood as the tunnelling process of a quasi-edge mode through the local gap on Krylov chain. This picture provides a systematically method to obtain the effective prethermal Hamiltonian.

cond-mat.str-el↗

arXiv2024

Large Transformers are Better EEG Learners

Pre-trained large transformer models have achieved remarkable performance in the fields of natural language processing and computer vision. However, the limited availability of public electroencephalogram (EEG) data presents a unique challenge for extending the success of these models to EEG-based tasks. To address this gap, we propose AdaCT, plug-and-play Adapters designed for Converting Time series data into spatio-temporal 2D pseudo-images or text forms. Essentially, AdaCT-I transforms multi-channel or lengthy single-channel time series data into spatio-temporal 2D pseudo-images for fine-tuning pre-trained vision transformers, while AdaCT-T converts short single-channel data into text for fine-tuning pre-trained language transformers. The proposed approach allows for seamless integration of pre-trained vision models and language models in time series decoding tasks, particularly in EEG data analysis. Experimental results on diverse benchmark datasets, including Epileptic Seizure Recognition, Sleep-EDF, and UCI HAR, demonstrate the superiority of AdaCT over baseline methods. Overall, we provide a promising transfer learning framework for leveraging the capabilities of pre-trained vision and language models in EEG-based tasks, thereby advancing the field of time series decoding and enhancing interpretability in EEG data analysis. Our code will be available at https://github.com/wangbxj1234/AdaCE.

eess.SP↗

arXiv2024

Broadband NIR photon upconversion generates NIR persistent luminescence for bioimaging

Upconversion persistent luminescence (UCPL) phosphors that can be directly charged by near-infrared (NIR) light have gained considerable attention due to their promising applications ranging from photonics to biomedicine. However, current lanthanide-based UCPL phosphors show small absorption cross-sections and low upconversion charging efficiency. The development of UCPL phosphors faces challenges of lacking flexible upconversion charging pathways and poor design flexibility. Herein, we discovered a new lattice defect-mediated broadband photon upconversion process and the accompanied NIR-to-NIR UCPL in Cr-doped zinc gallate nanoparticles. The zinc gallate nanoparticles can be directly activated by broadband NIR light in the 700-1000 nm range to produce persistent luminescence at about 700 nm, which is also readily enhanced by rationally tailoring the lattice defects in the phosphors. This proposed UCPL phosphors achieved a signal-to-background ratio of over 200 in bioimaging by efficiently avoiding interference from autofluorescence and light scattering. Our findings reported the lattice defect-mediated photon upconversion for the first time, which significantly expanded the horizons for the flexible design of NIR-to-NIR UCPL phosphors toward broad applications.

physics.optics↗

arXiv2024

Research on Mobile Network High-precision Absolute Time Synchronization based on TAP

With the development of mobile communication and industrial internet technologies, the demand for robust absolute time synchronization based on network for diverse scenarios is significantly growing. TAP is a novel network timing method that aims to achieve sub-microsecond synchronization over air interface. This paper investigates the improvement and end-to-end realization of TAP. This paper first analyzes the effectiveness and deficiencies of TAP by establishing an equivalent clock model which evaluates TAP from timing error composition and allan variance. Second, this paper proposes a detailed base station and terminal design and the corresponding improvement of TAP. Both hardware compensation and protocol software design are taken into account so as to minimize timing error and system cost while maximizing compatibility with 3GPP. Finally, this paper presents a TAP end-to-end 5G prototype system developed based on software defined radio base station and COTS baseband module. The field test results show that the proposed scheme effectively solves the problems of TAP in application and robustly achieves 200ns level timing accuracy in various situations. The average accuracy with long observations can reach 1 nanosecond. It is 2$\sim$3 orders of magnitude better than common network timing methods, including NTP, PTP and the original TAP.

cs.NI↗

arXiv2024

Cross-Tokamak Deployment Study of Plasma Disruption Predictors Based on Convolutional Autoencoder

In the initial stages of operation for future tokamak, facing limited data availability, deploying data-driven disruption predictors requires optimal performance with minimal use of new device data. This paper studies the issue of data utilization in data-driven disruption predictor during cross tokamak deployment. Current predictors primarily employ supervised learning methods and require a large number of disruption and non-disruption shots for training. However, the scarcity and high cost of obtaining disruption shots for future tokamaks result in imbalanced training datasets, reducing the performance of supervised learning predictors. To solve this problem, we propose the Enhanced Convolutional Autoencoder Anomaly Detection (E-CAAD) predictor. E-CAAD can be only trained by normal samples from non-disruption shots and can also be trained by disruption precursor samples when disruption shots occur. This model not only overcomes the sample imbalance in supervised learning predictors, but also overcomes the inefficient dataset utilization faced by traditional anomaly detection predictors that cannot use disruption precursor samples for training, making it more suitable for the unpredictable datasets of future tokamaks. Compared to traditional anomaly detection predictor, the E-CAAD predictor performs better in disruption prediction and is deployed faster on new devices. Additionally, we explore strategies to accelerate deployment of E-CAAD predictor on the new device by using data from existing devices. Two deployment strategies are presented: mixing data from existing devices and fine-tuning the predictor trained on existing devices. Our comparisons indicate that the data from existing device can accelerate the deployment of predictor on new device. Notably, the fine-tuning strategy yields the fastest deployment on new device among the designed strategies.

physics.plasm-ph↗

arXiv2023

Ball Mill Fault Prediction Based on Deep Convolutional Auto-Encoding Network

Ball mills play a critical role in modern mining operations, making their bearing failures a significant concern due to the potential loss of production efficiency and economic consequences. This paper presents an anomaly detection method based on Deep Convolutional Auto-encoding Neural Networks (DCAN) for addressing the issue of ball mill bearing fault detection. The proposed approach leverages vibration data collected during normal operation for training, overcoming challenges such as labeling issues and data imbalance often encountered in supervised learning methods. DCAN includes the modules of convolutional feature extraction and transposed convolutional feature reconstruction, demonstrating exceptional capabilities in signal processing and feature extraction. Additionally, the paper describes the practical deployment of the DCAN-based anomaly detection model for bearing fault detection, utilizing data from the ball mill bearings of Wuhan Iron & Steel Resources Group and fault data from NASA's bearing vibration dataset. Experimental results validate the DCAN model's reliability in recognizing fault vibration patterns. This method holds promise for enhancing bearing fault detection efficiency, reducing production interruptions, and lowering maintenance costs.

cs.LG↗

arXiv2023

Cross-tokamak Disruption Prediction based on Physics-Guided Feature Extraction and domain adaptation

The high acquisition cost and the significant demand for disruptive discharges for data-driven disruption prediction models in future tokamaks pose an inherent contradiction in disruption prediction research. In this paper, we demonstrated a novel approach to predict disruption in a future tokamak using only a few discharges. The first step is to use the existing understanding of physics to extract physics-guided features from the diagnostic signals of each tokamak, called physics-guided feature extraction (PGFE). The second step is to align a few data from the future tokamak (target domain) and a large amount of data from existing tokamak (source domain) based on a domain adaptation algorithm called CORrelation ALignment (CORAL). It is the first attempt at applying domain adaptation in the task of disruption prediction. PGFE has been successfully applied in J-TEXT to predict disruption with excellent performance. PGFE can also reduce the data volume requirements due to extracting the less device-specific features, thereby establishing a solid foundation for cross-tokamak disruption prediction. We have further improved CORAL (supervised CORAL, S-CORAL) to enhance its appropriateness in feature alignment for the disruption prediction task. To simulate the existing and future tokamak case, we selected J-TEXT as the existing tokamak and EAST as the future tokamak, which has a large gap in the ranges of plasma parameters. The utilization of the S-CORAL improves the disruption prediction performance on future tokamak. Through interpretable analysis, we discovered that the learned knowledge of the disruption prediction model through this approach exhibits more similarities to the model trained on large data volumes of future tokamak.

physics.plasm-ph↗

arXiv2023

How Biomimetic Morphing Dorsal Fin Affects the Swimming Performance of a Free-swimming Tuna Robot

It is well known that tuna fish in the ocean can dynamically morph their median fins to achieve optimal hydrodynamic performance, e.g. linear acceleration and maneuverability. In this study, based on the previous studies about the median fin's hydrodynamic effects focusing on tethered conditions, we continue to explore the hydrodynamic function of tuna morphing dorsal fin in free swimming conditions for better approaching real-life situations.Here, we developed a tuna-inspired robotic fish platform that can swim independently in three dimensions, equipped with a biomimetic morphing dorsal fin magnetically attached to the robotic fish. Based on the free-swimming robotic fish platform, we investigated how the erected dorsal fin affects the speed, cost of transport (COT), and robotic fish's yaw angle at different frequencies and amplitudes. The erected dorsal fin plays a positive role in improving the yaw stability of robotic fish. However, it shows little influence on the speed and COT in our test. This remains to be further investigated in the future.

cs.RO↗

arXiv2023

Multichannel consecutive data cross-extraction with 1DCNN-attention for diagnosis of power transformer

Power transformer plays a critical role in grid infrastructure, and its diagnosis is paramount for maintaining stable operation. However, the current methods for transformer diagnosis focus on discrete dissolved gas analysis, neglecting deep feature extraction of multichannel consecutive data. The unutilized sequential data contains the significant temporal information reflecting the transformer condition. In light of this, the structure of multichannel consecutive data cross-extraction (MCDC) is proposed in this article in order to comprehensively exploit the intrinsic characteristic and evaluate the states of transformer. Moreover, for the better accommodation in scenario of transformer diagnosis, one dimensional convolution neural network attention (1DCNN-attention) mechanism is introduced and offers a more efficient solution given the simplified spatial complexity. Finally, the effectiveness of MCDC and the superior generalization ability, compared with other algorithms, are validated in experiments conducted on a dataset collected from real operation cases of power transformer. Additionally, the better stability of 1DCNN-attention has also been certified.

cs.LG↗

arXiv2023

Fast Approximation of the Shapley Values Based on Order-of-Addition Experimental Designs

Shapley value is originally a concept in econometrics to fairly distribute both gains and costs to players in a coalition game. In the recent decades, its application has been extended to other areas such as marketing, engineering and machine learning. For example, it produces reasonable solutions for problems in sensitivity analysis, local model explanation towards the interpretable machine learning, node importance in social network, attribution models, etc. However, its heavy computational burden has been long recognized but rarely investigated. Specifically, in a $d$-player coalition game, calculating a Shapley value requires the evaluation of $d!$ or $2^d$ marginal contribution values, depending on whether we are taking the permutation or combination formulation of the Shapley value. Hence it becomes infeasible to calculate the Shapley value when $d$ is reasonably large. A common remedy is to take a random sample of the permutations to surrogate for the complete list of permutations. We find an advanced sampling scheme can be designed to yield much more accurate estimation of the Shapley value than the simple random sampling (SRS). Our sampling scheme is based on combinatorial structures in the field of design of experiments (DOE), particularly the order-of-addition experimental designs for the study of how the orderings of components would affect the output. We show that the obtained estimates are unbiased, and can sometimes deterministically recover the original Shapley value. Both theoretical and simulations results show that our DOE-based sampling scheme outperforms SRS in terms of estimation accuracy. Surprisingly, it is also slightly faster than SRS. Lastly, real data analysis is conducted for the C. elegans nervous system and the 9/11 terrorist network.

stat.ML↗

arXiv2023

Many-body non-Hermitian skin effect under dynamic gauge coupling

We study an atom-cavity hybrid system where fermionic atoms in a one-dimensional lattice are subject to a cavity-induced dynamic gauge potential. The gauge coupling leads to highly-degenerate steady states in which the fermions accumulate to one edge of the lattice under an open boundary condition. Such a phenomenon originates from the many-body Liouvillian superoperator of the system, which, being intrinsically non-Hermitian, is unstable against boundary perturbations and manifests the non-Hermitian skin effect. Contrary to the single-body case, the steady state of a multi-atom system is approached much slower under the open boundary condition, as the long-time damping of the cavity mode exhibits distinct rates at different times. This stage-wise slowdown is attributed to the competition between light-assisted hopping and the dynamic gauge coupling, which significantly reduces the steady-state degeneracy under the open boundary condition, as distinct hosts of quasi-steady states dominate the dynamics at different time scales.

cond-mat.quant-gas↗

arXiv2023

How wavelength affects the hydrodynamic performance of two accelerating mirror-symmetric slender swimmers

Fish schools are capable of simultaneous linear acceleration. To reveal the underlying hydrodynamic mechanism, we numerically investigate how Reynolds number $ Re = 1000 - 2000 $, Strouhal number $ St = 0.2 - 0.7 $ and wavelength $ λ= 0.5 - 2 $ affect the mean net thrust and net propulsive efficiency of two side-by-side hydrofoils undulating in anti-phase. In total, $ 550 $ cases are simulated using immersed boundary method. The thrust increases significantly with wavelength and Strouhal number, yet only slightly with the Reynolds number. We apply a symbolic regression algorithm to formulate this relationship. Furthermore, we find that mirror-symmetric schooling can achieve a \textit{net} thrust more than ten times that of a single swimmer, especially at low Reynolds numbers. The highest efficiency is obtained at $ St = 0.5 $ and $ λ= 1.2 $, where $ St $ is consistent with that observed in the linear-accelerating natural swimmers, \eg Crevalle jack. Six distinct flow structures are identified. The highest thrust corresponds to an asymmetric flow pattern, whereas the highest efficiency occurs when the flow is symmetric with converging vortex streets.

physics.flu-dyn↗

arXiv2023

Gauge Violation Spectroscopy in Synthetic Gauge Theories

Recently synthetic gauge fields have been implemented on quantum simulators. Unlike the gauge fields in the real world, in synthetic gauge fields, the gauge charge can fluctuate and gauge invariance can be violated, which leading rich physics unexplored before. In this work, we propose the gauge violation spectroscopy as a useful experimentally accessible measurement in the synthetic gauge theories. We show that the gauge violation spectroscopy exhibits no dispersion. Using three models as examples, two of them can be exactly solved by bosonization, and one has been realized in experiment, we further demonstrate the gauge violation spectroscopy can be used to detect the confinement and deconfinement phases. In the confinement phase, it shows a delta function behavior, while in the deconfinement phase, it has a finite width.

cond-mat.quant-gas↗

arXiv2023

Non-equilibrium phases of Fermi gas inside a cavity with imbalanced pumping

In this work, we investigate the non-equilibrium dynamics of one-dimensional spinless fermions loaded in a cavity with imbalanced pumping lasers. Our study is motivated by previous work on a similar setup using bosons, and we explore the unique properties of fermionic systems in this context. By considering the imbalance in the pumping, we find that the system exhibits multiple superradiant steady phases and an unstable phase. Furthermore, by making use of the hysteresis structure of superradiant phases, we propose a unidirectional topological pumping. Unlike the usual topological pumping in which the driving protocol breaks time reversal symmetry, the driving protocol can be time reversal invariant in our proposal.

cond-mat.quant-gas↗

arXiv2023

Observation of microscopic confinement dynamics by a tunable topological $θ$-angle

The topological $θ$-angle is central to the understanding of a plethora of phenomena in condensed matter and high-energy physics such as the strong CP problem, dynamical quantum topological phase transitions, and the confinement--deconfinement transition. Difficulties arise when probing the effects of the topological $θ$-angle using classical methods, in particular through the appearance of a sign problem in numerical simulations. Quantum simulators offer a powerful alternate venue for realizing the $θ$-angle, which has hitherto remained an outstanding challenge due to the difficulty of introducing a dynamical electric field in the experiment. Here, we report on the experimental realization of a tunable topological $θ$-angle in a Bose--Hubbard gauge-theory quantum simulator, implemented through a tilted superlattice potential that induces an effective background electric field. We demonstrate the rich physics due to this angle by the direct observation of the confinement--deconfinement transition of $(1+1)$-dimensional quantum electrodynamics. Using an atomic-precision quantum gas microscope, we distinguish between the confined and deconfined phases by monitoring the real-time evolution of particle--antiparticle pairs, which exhibit constrained (ballistic) propagation for a finite (vanishing) deviation of the $θ$-angle from $π$. Our work provides a major step forward in the realization of topological terms on modern quantum simulators, and the exploration of rich physics they have been theorized to entail.

cond-mat.quant-gas↗

arXiv2023

Review of X-ray pulsar spacecraft autonomous navigation

This article provides a review on X-ray pulsar-based navigation (XNAV). The review starts with the basic concept of XNAV, and briefly introduces the past, present and future projects concerning XNAV. This paper focuses on the advances of the key techniques supporting XNAV, including the navigation pulsar database, the X-ray detection system, and the pulse time of arrival estimation. Moreover, the methods to improve the estimation performance of XNAV are reviewed. Finally, some remarks on the future development of XNAV are provided.

astro-ph.IM↗