arXiv subjects

Yuanyuan Chen

Publications and source records attributed to Yuanyuan Chen.

At least 37 records · Page 2Linked to original sources

arXiv2024

"Stumbling-to-Fetters" mechanism and Virginia Creeper model in hydrogel for designing bionic cardiovascular system

Manufacturing hydrogels with identical electrochemical properties are typically riddled with unresolved inquiries and challenges. Here, we utilized ultra-light graphene flakes to trace the influence of convection phenomena during reactions on hydrogels' formation and structural non-uniformity, elucidating its mechanisms. Furthermore, we confirmed that an external electric field induced the orientation of functional groups of hydrogels along the direction of this field, revealing the mechanism of its influence on the structural non-uniformity and electrochemical properties of hydrogels. Additionally, we discovered that ion diffusion was "Stumbling-to-Fetters" by the functional groups on the polymer chains within the hydrogel, unveiling this mechanism and developing the Virginia Creeper (VC) model for hydrogels. We demonstrated the scalability and application of the VC model. Furthermore, we proposed a molecular-ion diffusion and current decay equation to describe the electrochemical properties of hydrogels. As an application of the VC model, we developed a bionic cardiovascular system and proved its potential to seamlessly interface with living organisms and generate bio-like bioelectricity. Our findings provide novel insights into triboelectricity and guidance for producing hydrogels with identical electrochemical properties, and offer a new pathway for bioelectric generation and the design of new hydrogel devices.

cond-mat.soft↗

arXiv2024

Fast transport and splitting of spin-orbit-coupled spin-1 Bose-Einstein Condensates

In this study, we investigate the dynamics of tunable spin-orbit-coupled spin-1 Bose-Einstein condensates confined within a harmonic trap, focusing on rapid transport, spin manipulation, and splitting dynamics. Using shortcuts to adiabaticity, we design time-dependent trap trajectories and spin-orbit-coupling strength to facilitate fast transport with simultaneous spin flip. Additionally, we showcase the creation of spin-dependent coherent states via engineering the spin-orbit-coupling strength. To deepen our understanding, we elucidate non-adiabatic transport and associated spin dynamics, contrasting them with simple scenarios characterized by constant spin-orbit coupling and trap velocity. Furthermore, we discuss the transverse Zeeman potential and nonlinear effect induced by interatomic interactions using the Gross-Pitaevskii equation, highlighting the stability and feasibility of the proposed protocols for the state-of-the-art experiments with cold atoms.

cond-mat.quant-gas↗

arXiv2024

Overcoming Catastrophic Forgetting by Exemplar Selection in Task-oriented Dialogue System

Intelligent task-oriented dialogue systems (ToDs) are expected to continuously acquire new knowledge, also known as Continual Learning (CL), which is crucial to fit ever-changing user needs. However, catastrophic forgetting dramatically degrades the model performance in face of a long streamed curriculum. In this paper, we aim to overcome the forgetting problem in ToDs and propose a method (HESIT) with hyper-gradient-based exemplar strategy, which samples influential exemplars for periodic retraining. Instead of unilaterally observing data or models, HESIT adopts a profound exemplar selection strategy that considers the general performance of the trained model when selecting exemplars for each task domain. Specifically, HESIT analyzes the training data influence by tracing their hyper-gradient in the optimization process. Furthermore, HESIT avoids estimating Hessian to make it compatible for ToDs with a large pre-trained model. Experimental results show that HESIT effectively alleviates catastrophic forgetting by exemplar selection, and achieves state-of-the-art performance on the largest CL benchmark of ToDs in terms of all metrics.

cs.LG↗

arXiv2024

Fully Automated OCT-based Tissue Screening System

This study introduces a groundbreaking optical coherence tomography (OCT) imaging system dedicated for high-throughput screening applications using ex vivo tissue culture. Leveraging OCT's non-invasive, high-resolution capabilities, the system is equipped with a custom-designed motorized platform and tissue detection ability for automated, successive imaging across samples. Transformer-based deep learning segmentation algorithms further ensure robust, consistent, and efficient readouts meeting the standards for screening assays. Validated using retinal explant cultures from a mouse model of retinal degeneration, the system provides robust, rapid, reliable, unbiased, and comprehensive readouts of tissue response to treatments. This fully automated OCT-based system marks a significant advancement in tissue screening, promising to transform drug discovery, as well as other relevant research fields.

physics.med-ph↗

arXiv2024

Supercurrent-carrying supersolid in spin-orbit-coupled Bose-Einstein condensates

One of brilliant achievements in spin-orbit-coupled Bose-Einstein condensates is the discovery and observation of the supersolid stripe states. So far, all studied supersolid stripe states do not carry supercurrent. In this work, we reveal the existence of supercurrent-carrying supersolids in spin-orbit-coupled Bose-Einstein condensates. The supersolid family has a parabolic-like dispersion relation and carries supercurrent which is proportional to the quasimomentum. Energetic and dynamical instabilities can break supercurrent-carrying ability of this supersolid family. An insightful interpretation of the dynamical instability of supercurrent-carrying supersolids from the pure plane-wave phase is provided.

cond-mat.quant-gas↗

arXiv2023

Active Noise Control Portable Device Design

While our world is filled with its own natural sounds that we can't resist enjoying, it is also chock-full of other sounds that can be irritating, this is noise. Noise not only influences the working efficiency but also the human's health. The problem of reducing noise is one of great importance and great difficulty. The problem has been addressed in many ways over the years. The current methods for noise reducing mostly rely on the materials and transmission medium, which are only effective to some extent for the high frequency noise. However, the effective reduction noise method especially for low frequency noise is very limited. Here we come up with a noise reduction system consist of a sensor to detect the noise in the environment. Then the noise will be sent to an electronic control system to process the noise, which will generate a reverse phase frequency signal to counteract the disturbance. Finally, the processed smaller noise will be broadcasted by the speaker. Through this smart noise reduction system, even the noise with low-frequency can be eliminated. The system is also integrated with sleep tracking and music player applications. It can also remember and store settings for the same environment, sense temperature, and smart control of home furniture, fire alarm, etc. This smart system can transfer data easily by Wi-Fi or Bluetooth and controlled by its APP. In this project, we will present a model of the above technology which can be used in various environments to prevent noise pollution and provide a solution to the people who have difficulties finding a peaceful and quiet environment for sleep, work or study.

cs.SD↗

arXiv2023

Toughness and spectral radius in graphs

The Brouwer's toughness conjecture states that every $d$-regular connected graph always has $t(G)>\frac{d}λ-1$ where $λ$ is the second largest absolute eigenvalue of the adjacency matrix. In 1988, Enomoto introduced a variation of toughness $τ(G)$ of a graph $G$. By incorporating the variation of toughness and spectral conditions, we provide spectral conditions for a graph to be $τ$-tough ($τ\geq 2$ is an integer) and to be $τ$-tough ($\frac{1}τ$ is a positive integer) with minimum degree $δ$, respectively. Additionally, we also investigate a analogous problem concerning balanced bipartite graphs.

math.CO↗

arXiv2023

Sciences with the 2.5-meter Wide Field Survey Telescope (WFST)

The Wide Field Survey Telescope (WFST) is a dedicated photometric surveying facility being built jointly by the University of Science and Technology of China and the Purple Mountain Observatory. It is equipped with a 2.5-meter diameter primary mirror, an active optics system, and a mosaic CCD camera with 0.73 gigapixels on the primary focal plane for high-quality image capture over an FOV of 6.5-square-degree. It is anticipated that WFST will be set up at the Lenghu site in the summer of 2023 and begin to observe the northern sky in four optical bands (u, g, r, and i) with a range of cadences, from hourly/daily in the Deep High-Cadence Survey (DHS) program to semiweekly in the Wide-Field Survey (WFS) program, three months later. During a photometric night, a nominal 30 s exposure in the WFS program will reach a depth of 22.27, 23.32, 22.84, and 22.31 (AB magnitudes) in these four bands, respectively, allowing for the detection of a tremendous amount of transients in the low-z universe and a systematic investigation of the variability of Galactic and extragalactic objects. In the DHS program, intranight 90 s exposures as deep as 23 (u) and 24 mag (g), in combination with target of opportunity follow-ups, will provide a unique opportunity to explore energetic transients in demand for high sensitivities, including the electromagnetic counterparts of gravitational wave events, supernovae within a few hours of their explosions, tidal disruption events and fast, luminous optical transients even beyond a redshift of unity. In addition, the final 6-year co-added images, anticipated to reach g=25.8 mag in WFS or 1.5 mags deeper in DHS, will be of fundamental importance to general Galactic and extragalactic science. The highly uniform legacy surveys of WFST will serve as an indispensable complement to those of LSST that monitor the southern sky.

astro-ph.IM↗

arXiv2023

Aggregating Intrinsic Information to Enhance BCI Performance through Federated Learning

Insufficient data is a long-standing challenge for Brain-Computer Interface (BCI) to build a high-performance deep learning model. Though numerous research groups and institutes collect a multitude of EEG datasets for the same BCI task, sharing EEG data from multiple sites is still challenging due to the heterogeneity of devices. The significance of this challenge cannot be overstated, given the critical role of data diversity in fostering model robustness. However, existing works rarely discuss this issue, predominantly centering their attention on model training within a single dataset, often in the context of inter-subject or inter-session settings. In this work, we propose a hierarchical personalized Federated Learning EEG decoding (FLEEG) framework to surmount this challenge. This innovative framework heralds a new learning paradigm for BCI, enabling datasets with disparate data formats to collaborate in the model training process. Each client is assigned a specific dataset and trains a hierarchical personalized model to manage diverse data formats and facilitate information exchange. Meanwhile, the server coordinates the training procedure to harness knowledge gleaned from all datasets, thus elevating overall performance. The framework has been evaluated in Motor Imagery (MI) classification with nine EEG datasets collected by different devices but implementing the same MI task. Results demonstrate that the proposed frame can boost classification performance up to 16.7% by enabling knowledge sharing between multiple datasets, especially for smaller datasets. Visualization results also indicate that the proposed framework can empower the local models to put a stable focus on task-related areas, yielding better performance. To the best of our knowledge, this is the first end-to-end solution to address this important challenge.

eess.SP↗

arXiv2023

Quantum phases in spin-orbit-coupled Floquet spinor Bose gases

We propose a spin-orbit-coupled Floquet spinor Bose-Einstein condensate (BEC) which can be implemented by Floquet engineering of a quadratic Zeeman field. The Floquet spinor BEC has a Bessel-function-modulated Rabi frequency and a Floquet-induced spin-exchange interaction. The quantum phase diagram of the spin-orbit-coupled Floquet spinor BEC is investigated by considering antiferromagnetic or ferromagnetic spin-spin interactions. In comparison with the usual spin-orbit-coupled spin-1 BEC, we find that a stripe phase for antiferromagnetic interactions can exist in a large quadratic Zeeman field regime, and a different stripe phase with an experimentally favorable contrast for ferromagnetic interactions is uncovered.

cond-mat.quant-gas↗

arXiv2023

Waiting, Banning, and Embracing: An Empirical Analysis of Adapting Policies for Generative AI in Higher Education

Generative AI tools such as ChatGPT have recently gained significant attention in higher education. This study aims to understand how universities establish policies regarding the use of AI tools and explore the factors that influence their decisions. Our study examines ChatGPT policies implemented at universities around the world, including their existence, content, and issuance dates. Specifically, we analyzed the top 500 universities according to the 2022 QS World University Rankings. Our findings indicate that there is significant variation in university policies. Less than one-third of the universities included in the study had implemented ChatGPT policies. Of the universities with ChatGPT policies, approximately 67 percent embraced ChatGPT in teaching and learning, more than twice the number of universities that banned it. The majority of the universities that ban the use of ChatGPT in assessments allow individual instructors to deviate from this restrictive policy. Our empirical analysis identifies several factors that are significantly and positively correlated with a university's likelihood of having a ChatGPT policy, including the university's academic reputation score, being in an English-speaking country, and the general public attitudes toward ChatGPT. In addition, we found that a university's likelihood of having a ban policy is positively associated with faculty student ratio, citations, and the English-speaking country dummy, while negatively associated with the number of peer universities within the same country that have banned ChatGPT. We discuss the challenges faced by universities based our empirical findings.

cs.CY↗

arXiv2023

Efficient Training of Large-scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout

Artificial intelligence (AI)-empowered industrial fault diagnostics is important in ensuring the safe operation of industrial applications. Since complex industrial systems often involve multiple industrial plants (possibly belonging to different companies or subsidiaries) with sensitive data collected and stored in a distributed manner, collaborative fault diagnostic model training often needs to leverage federated learning (FL). As the scale of the industrial fault diagnostic models are often large and communication channels in such systems are often not exclusively used for FL model training, existing deployed FL model training frameworks cannot train such models efficiently across multiple institutions. In this paper, we report our experience developing and deploying the Federated Opportunistic Block Dropout (FEDOBD) approach for industrial fault diagnostic model training. By decomposing large-scale models into semantic blocks and enabling FL participants to opportunistically upload selected important blocks in a quantized manner, it significantly reduces the communication overhead while maintaining model performance. Since its deployment in ENN Group in February 2022, FEDOBD has served two coal chemical plants across two cities in China to build industrial fault prediction models. It helped the company reduce the training communication overhead by over 70% compared to its previous AI Engine, while maintaining model performance at over 85% test F1 score. To our knowledge, it is the first successfully deployed dropout-based FL approach.

cs.LG↗

arXiv2022

The Sparse Solution to $\mathcal{KS}$-Tensor Complementarity Problems

In view of the KS-tensor complementarity problem, the sparse solution of this problem is studied. Due to the nonconvexity and noncontinuity of the l_0-norm, it is a NP hard problem to find the sparse solution of the KS-tensor complementarity problem. In order to solve this problem, we transform it into a polynomial programming problem with constraints. Then we use the sequential quadratic programming (SQP) algorithm to solve this transformed problem. Numerical results show that the SQP algorithm can find the sparse solutions of the KS-tensor complementarity problem effectively.

math.OC↗

arXiv2022

Elementary excitations in a spin-orbit-coupled spin-1 Bose-Einstein condensate

While a spin-orbit-coupled spin-1 Bose-Einstein condensate has been experimentally observed, its elementary excitations remain unclear in the stripe phase. Here, we systematically study the elementary excitations in three distinct phases of a spin-orbit-coupled spin-1 Bose-Einstein condensate. We find that the excitation spectrum as well as the corresponding static response function and structure factor depend strongly on spin-orbit coupling parameters such as the quadratic Zeeman field and the Rabi frequency. In the stripe phase, besides two gapless Goldstone modes, we show the existence of roton excitations. Finally, we demonstrate that quantum phase transitions between these different phases including the zero-momentum, plane wave and stripe phases are characterized by the sound velocities and the quantum depletion.

cond-mat.quant-gas↗

arXiv2022

Quantum generative adversarial learning for simultaneous multiparameter estimation

Generative adversarial learning is currently one of the most prolific fields in artificial intelligence due to its great performance in a variety of challenging tasks such as photorealistic image and video generation. While a quantum version of generative adversarial learning has emerged that promises exponential advantages over its classical counterpart, its experimental implementation and potential applications with accessible quantum technologies remain explored little. Here, we report an experimental demonstration of quantum generative adversarial learning with the assistance of adaptive feedback that is based on stochastic gradient descent algorithm. Its performance is explored by applying this technique to the adaptive characterization of quantum dynamics and simultaneous estimation of multiple phases. These results indicate the intriguing advantages of quantum generative adversarial learning even in the presence of deleterious noise, and pave the way towards quantum-enhanced information processing applications.

quant-ph↗

arXiv2022

Quantum Wiener-Khinchin theorem for spectral-domain optical coherence tomography

Wiener-Khinchin theorem, the fact that the autocorrelation function of a time process has a spectral decomposition given by its power spectrum intensity, can be used in many disciplines. However, the applications based on a quantum counterpart of Wiener-Khinchin theorem that provides a translation between time-energy degrees of freedom of biphoton wavefunction still remains relatively unexplored. Here, we use a quantum Wiener-Khinchin theorem (QWKT) to state that two-photon joint spectral intensity and the cross-correlation of two-photon temporal signal can be connected by making a Fourier transform. The mathematically-defined QWKT is experimentally demonstrated in frequency-entangled two-photon Hong-Ou-Mandel (HOM) interference with the assistance of spectrally-resolved detection. We apply this method to spectral-domain quantum optical coherence tomography that detects thickness-induced optical delays in a transparent sample, and show that our method suffices to achieve great advantages in measurement precision within a wide dynamic range and capturing time over the conventional HOM interferometric schemes. These results may significantly facilitate the use of QWKT for quantum information processing and quantum interferometric spectroscopy.

quant-ph↗

arXiv2021

Quantum interferometric two-photon excitation spectroscopy

Two-photon excitation spectroscopy is a nonlinear technique that has gained rapidly in interest and significance for studying the complex energy-level structure and transition probabilities of materials. While the conventional spectroscopy based on tunable classical light has been long established, quantum light provides an alternative way towards excitation spectroscopy with potential advantages in temporal and spectral resolution, as well as reduced photon fluxes. By using a quantum Fourier transform that connects the sum-frequency intensity and N00N-state temporal interference, we present an approach for quantum interferometric two-photon excitation spectroscopy. Our proposed protocol overcomes the difficulties of engineering two-photon joint spectral intensities and fine-tuned absorption-frequency selection. These results may significantly facilitate the use of quantum interferometric spectroscopy for extracting the information about the electronic structure of the two-photon excited-state manifold of atoms or molecules, in a "single-shot" measurement. This may be particularly relevant for photon-sensitive biological and chemical samples.

quant-ph↗

arXiv2021

Temporal dissipative structures in optical Kerr resonators with transient loss fluctuation

Dissipative structures are the result of spontaneous symmetry breaking in a dynamic open system, which is induced by either the nonlinear effect or loss fluctuations. While optical temporal dissipative solitons in nonlinear Kerr cavities has been widely studied, they are operated in a red-detuned regime that is non-trivial to access. Here, we demonstrate an emergent dissipative soliton state in optical cavities in the presence of loss fluctuations, which is accessible by self-evolution of the system and is operated in resonance. We numerically investigate both the effect of loss modulation and the effect of saturable absorption, based on a standard dissipative and Kerr-nonlinear microresonator model, and observe stable soliton states in a close-to-zero detuning region. The self-starting soliton state working in resonance is potentially of wide interest, which would not only ease the operation for ultrafast temporal soliton generation, but also imply a high conversion efficiency for soliton micro-combs.

physics.optics↗