arXiv subjects

M. Li

Publications and source records attributed to M. Li.

At least 37 records · Page 2Linked to original sources

arXiv2024

Single-molecule Surface-Induced Fluorescence Attenuation Based on Reduced Graphene Oxide

Single-molecule surface-induced fluorescence attenuation (smSIFA) is a precise method for studying the vertical movement of biological macromolecules using two-dimensional material acceptors. Unlike other methods, smSIFA is not influenced by the planar motion of membranes or proteins. However, the detection range and accuracy of vertical movement are dependent on the properties of these two-dimensional materials. Recently, smSIFA utilizing graphene oxide and graphene has significantly advanced the study of biomacromolecules, although the detection range is restricted by their inherent quenching distances. Modifying these distances necessitates the replacement of the medium material, which presents challenges in material selection and preparation. Consequently, there is a pressing need to develop controllable materials for smSIFA applications. In this study, we enhance the smSIFA technique using graphene oxide as the medium acceptor through thermal reduction. By adjusting the reduction temperature, we prepare reduced graphene oxides at varying degrees of reduction, thus fine-tuning the quenching distances. The adjustment of these distances is measured using fluorescently labeled DNA. This modified smSIFA approach, employing reduced graphene oxide, is then applied to observe conformational changes in the Holliday junction, demonstrating the enhanced detection capabilities of reduced graphene oxide.

physics.bio-ph↗

arXiv2024

High-fidelity universal gates in the $^{171}$Yb ground state nuclear spin qubit

Arrays of optically trapped neutral atoms are a promising architecture for the realization of quantum computers. In order to run increasingly complex algorithms, it is advantageous to demonstrate high-fidelity and flexible gates between long-lived and highly coherent qubit states. In this work, we demonstrate a universal high-fidelity gate-set with individually controlled and parallel application of single-qubit gates and two-qubit gates operating on the ground-state nuclear spin qubit in arrays of tweezer-trapped $^{171}$Yb atoms. We utilize the long lifetime, flexible control, and high physical fidelity of our system to characterize native gates using single and two-qubit Clifford and symmetric subspace randomized benchmarking circuits with more than 200 CZ gates applied to one or two pairs of atoms. We measure our two-qubit entangling gate fidelity to be 99.72(3)% (99.40(3)%) with (without) post-selection. In addition, we introduce a simple and optimized method for calibration of multi-parameter quantum gates. These results represent important milestones towards executing complex and general quantum computation with neutral atoms.

quant-ph↗

arXiv2024

Highly Efficient and Unsupervised Framework for Moving Object Detection in Satellite Videos

Moving object detection in satellite videos (SVMOD) is a challenging task due to the extremely dim and small target characteristics. Current learning-based methods extract spatio-temporal information from multi-frame dense representation with labor-intensive manual labels to tackle SVMOD, which needs high annotation costs and contains tremendous computational redundancy due to the severe imbalance between foreground and background regions. In this paper, we propose a highly efficient unsupervised framework for SVMOD. Specifically, we propose a generic unsupervised framework for SVMOD, in which pseudo labels generated by a traditional method can evolve with the training process to promote detection performance. Furthermore, we propose a highly efficient and effective sparse convolutional anchor-free detection network by sampling the dense multi-frame image form into a sparse spatio-temporal point cloud representation and skipping the redundant computation on background regions. Coping these two designs, we can achieve both high efficiency (label and computation efficiency) and effectiveness. Extensive experiments demonstrate that our method can not only process 98.8 frames per second on 1024x1024 images but also achieve state-of-the-art performance. The relabeled dataset and code are available at https://github.com/ChaoXiao12/Moving-object-detection-in-satellite-videos-HiEUM.

cs.CV↗

arXiv2024

Exploring the determinants on massive open online courses continuance learning intention in business toward accounting context

Massive open online courses (MOOC) have become important in the learning journey of college students and have been extensively implemented in higher education. However, there are few studies that investigated the willingness to continue using Massive open online courses (MOOC) in the field of business in higher education. Therefore, this paper proposes a comprehensive theoretical research framework based on the Theory of Planned Behavior (TPB). In the field of business, a representative accounting course is taken as an example. We adopt the questionnaire survey method and use the partial least squares structural equation model to analyze the collected feedback data from college students and test the hypotheses. This paper focuses on the potential influencing factors and mechanisms of the willingness to continuously use Massive open online courses (MOOC) in accounting. The results show that interface convenience (IC) and interface design aesthetics (IDA) have positive effects on user attitude (ATT). User attitude (ATT), perceived behavioral control (PBC), and subjective norms (SN) have positive effects on the continuance learning intention. In addition, academic self-efficacy (EF) not only significantly affects continuance learning intention (CI) but also moderates the relationship between the Theory of Planned Behavior (user attitude, perceived behavior control, subjective norms) and the continuance learning intention of accounting MOOC. Therefore, the Theory of Planned Behavior(TPB) is extended in social science accounting Massive open online courses environment. Based on these findings, this paper provides several theoretical and practical implications for researchers and practitioners of MOOC, accounting, and the design of learning systems in higher education contexts.

cs.CY↗

arXiv2024

Investigating the effects of precise mass measurements of Ru and Pd isotopes on machine learning mass modeling

Atomic masses are a foundational quantity in our understanding of nuclear structure, astrophysics and fundamental symmetries. The long-standing goal of creating a predictive global model for the binding energy of a nucleus remains a significant challenge, however, and prompts the need for precise measurements of atomic masses to serve as anchor points for model developments. We present precise mass measurements of neutron-rich Ru and Pd isotopes performed at the Californium Rare Isotope Breeder Upgrade facility at Argonne National Laboratory using the Canadian Penning Trap mass spectrometer. The masses of $^{108}$Ru, $^{110}$Ru and $^{116}$Pd were measured to a relative mass precision $δm/m \approx 10^{-8}$ via the phase-imaging ion-cyclotron-resonance technique, and represent an improvement of approximately an order of magnitude over previous measurements. These mass data were used in conjunction with the physically interpretable machine learning (PIML) model, which uses a mixture density neural network to model mass excesses via a mixture of Gaussian distributions. The effects of our new mass data on a Bayesian-updating of a PIML model are presented.

nucl-ex↗

arXiv2024

Beyond the Carnot Limit in the Internal Cycles of a Quantum Heat Engine under Finite Heat Reservoirs

We investigate, in an analytical fashion, quantum Carnot cycles of a microscopic heat engine coupled to two nite heat reservoirs, whose internal cycles could own higher e ciency than the standard Carnot limit without consuming extra quantum resources, e.g., coherence or squeezing properties. The engine runs time-dependently, involving both the internal and external cycles to collaboratively accomplish a complete Carnot cycle, and the e ciency of the engine depends on the reservoirs heat capacities and the working substance. Our analytical results of the maximum efficiency and the maximum power output clarify the mechanism behind the high performance of the microscopic engines, displaying the key roles played by the nite-sized heat reservoirs. Our proposal is generally valid for any microscopic thermodynamic system and fully feasible under current laboratory conditions.

cond-mat.stat-mech↗

arXiv2024

Iterative assembly of $^{171}$Yb atom arrays with cavity-enhanced optical lattices

Assembling and maintaining large arrays of individually addressable atoms is a key requirement for continued scaling of neutral-atom-based quantum computers and simulators. In this work, we demonstrate a new paradigm for assembly of atomic arrays, based on a synergistic combination of optical tweezers and cavity-enhanced optical lattices, and the incremental filling of a target array from a repetitively filled reservoir. In this protocol, the tweezers provide microscopic rearrangement of atoms, while the cavity-enhanced lattices enable the creation of large numbers of optical traps with sufficient depth for rapid low-loss imaging of atoms. We apply this protocol to demonstrate near-deterministic filling (99% per-site occupancy) of 1225-site arrays of optical traps. Because the reservoir is repeatedly filled with fresh atoms, the array can be maintained in a filled state indefinitely. We anticipate that this protocol will be compatible with mid-circuit reloading of atoms into a quantum processor, which will be a key capability for running large-scale error-corrected quantum computations whose durations exceed the lifetime of a single atom in the system.

quant-ph↗

arXiv2024

A Strategy for Preparing Quantum Squeezed States Using Reinforcement Learning

We propose a scheme leveraging reinforcement learning to engineer control fields for generating non-classical states. It is exemplified by the application to prepare spin-squeezed states for an open collective spin model where a linear control field is designed to govern the dynamics. The reinforcement learning agent determines the temporal sequence of control pulses, commencing from a coherent spin state in an environment characterized by dissipation and dephasing. Compared to the constant control scenario, this approach provides various control sequences maintaining collective spin squeezing and entanglement. It is observed that denser application of the control pulses enhances the performance of the outcomes. However, there is a minor enhancement in the performance by adding control actions. The proposed strategy demonstrates increased effectiveness for larger systems. Thermal excitations of the reservoir are detrimental to the control outcomes. Feasible experiments are suggested to implement this control proposal based on the comparison with the others. The extensions to continuous control problems and another quantum system are discussed. The replaceability of the reinforcement learning module is also emphasized. This research paves the way for its application in manipulating other quantum systems.

quant-ph↗

arXiv2024

Emergence of rapid solidification microstructure in additive manufacturing of a Magnesium alloy

Bioresorbable Mg-based alloys with low density, low elastic modulus, and excellent biocompatibility are outstanding candidates for temporary orthopedic implants. Coincidentally, metal additive manufacturing (AM) is disrupting the biomedical sector by providing fast access to patient-customized implants. Due to the high cooling rates associated with fusion-based AM techniques, they are often described as rapid solidification processes. However, conclusive observations or rapid solidification in metal AM -- attested by drastic microstructural changes induced by solute trapping, kinetic undercooling, or morphological transitions of the solid-liquid interface -- are scarce. Here we study the formation of banded microstructures during laser powder-bed fusion (LPBF) of a biomedical-grade Magnesium-rare earth alloy, combining advanced characterization and state-of-the-art thermal and phase-field modeling. Our experiments unambiguously identify microstructures as the result of an oscillatory banding instability known from other rapid solidification processes. Our simulations confirm that LPBF-relevant solidification conditions strongly promote the development of banded microstructures in a Mg-Nd alloy. Simulations also allow us to peer into the sub-micrometer nanosecond-scale details of the solid-liquid interface evolution giving rise to the distinctive banded patterns. Since rapidly solidified Mg alloys may exhibit significantly different mechanical and corrosion response compared to their cast counterparts, the ability to predict the emergence of rapid solidification microstructures (and to correlate them with local solidification conditions) may open new pathways for the design of bioresorbable orthopedic implants, not only fitted geometrically to each patient, but also optimized with locally-tuned mechanical and corrosion properties.

cond-mat.mtrl-sci↗

arXiv2024

Results from a Prototype TES Detector for the Ricochet Experiment

Coherent elastic neutrino-nucleus scattering (CE$ν$NS) offers valuable sensitivity to physics beyond the Standard Model. The Ricochet experiment will use cryogenic solid-state detectors to perform a precision measurement of the CE$ν$NS spectrum induced by the high neutrino flux from the Institut Laue-Langevin nuclear reactor. The experiment will employ an array of detectors, each with a mass of $\sim$30 g and a targeted energy threshold of 50 eV. Nine of these detectors (the "Q-Array") will be based on a novel Transition-Edge Sensor (TES) readout style, in which the TES devices are thermally coupled to the absorber using a gold wire bond. We present initial characterization of a Q-Array-style detector using a 1 gram silicon absorber, obtaining a baseline root-mean-square resolution of less than 40 eV.

physics.ins-det↗

arXiv2024

Deep Learning Based Event Reconstruction for Cyclotron Radiation Emission Spectroscopy

The objective of the Cyclotron Radiation Emission Spectroscopy (CRES) technology is to build precise particle energy spectra. This is achieved by identifying the start frequencies of charged particle trajectories which, when exposed to an external magnetic field, leave semi-linear profiles (called tracks) in the time-frequency plane. Due to the need for excellent instrumental energy resolution in application, highly efficient and accurate track reconstruction methods are desired. Deep learning convolutional neural networks (CNNs) - particularly suited to deal with information-sparse data and which offer precise foreground localization - may be utilized to extract track properties from measured CRES signals (called events) with relative computational ease. In this work, we develop a novel machine learning based model which operates a CNN and a support vector machine in tandem to perform this reconstruction. A primary application of our method is shown on simulated CRES signals which mimic those of the Project 8 experiment - a novel effort to extract the unknown absolute neutrino mass value from a precise measurement of tritium $β^-$-decay energy spectrum. When compared to a point-clustering based technique used as a baseline, we show a relative gain of 24.1% in event reconstruction efficiency and comparable performance in accuracy of track parameter reconstruction.

physics.ins-det↗

arXiv2023

Cyclotron Radiation Emission Spectroscopy of Electrons from Tritium Beta Decay and $^{83\rm m}$Kr Internal Conversion

Project 8 has developed a novel technique, Cyclotron Radiation Emission Spectroscopy (CRES), for direct neutrino mass measurements. A CRES-based experiment on the beta spectrum of tritium has been carried out in a small-volume apparatus. We provide a detailed account of the experiment, focusing on systematic effects and analysis techniques. In a Bayesian (frequentist) analysis, we measure the tritium endpoint as $18553^{+18}_{-19}$ ($18548^{+19}_{-19}$) eV and set upper limits of 155 (152) eV (90% C.L.) on the neutrino mass. No background events are observed beyond the endpoint in 82 days of running. We also demonstrate an energy resolution of $1.66\pm0.19$ eV in a resolution-optimized magnetic trap configuration by measuring $^{83\rm m}$Kr 17.8-keV internal-conversion electrons. These measurements establish CRES as a low-background, high-resolution technique with the potential to advance neutrino mass sensitivity.

nucl-ex↗

arXiv2023

Modeling and characterization of TES-based detectors for the Ricochet experiment

Coherent elastic neutrino-nucleus scattering (CE$ν$NS) offers a valuable approach in searching for physics beyond the Standard Model. The Ricochet experiment aims to perform a precision measurement of the CE$ν$NS spectrum at the Institut Laue-Langevin nuclear reactor with cryogenic solid-state detectors. The experiment plans to employ an array of cryogenic thermal detectors, each with a mass around 30 g and an energy threshold of sub-100 eV. The array includes nine detectors read out by Transition-Edge Sensors (TES). These TES based detectors will also serve as demonstrators for future neutrino experiments with thousands of detectors. In this article we present an update in the characterization and modeling of a prototype TES detector.

hep-ex↗

arXiv2023

Dynamics Reflects Quantum Phase Transition of Rabi Model

As the simplest and most fundamental model describing the interaction between light and matter, a breakdown in the rotating wave approximation of the Rabi model leads to phase transition versus coupling strength when the frequency of the qubit greatly surpasses that of the oscillator. Besides the phase transition revealed in the ground state, we show that the dynamics of physical quantities can reflect such a phase transition for this model. In addition to the excitation of the bosonic field in the ground state, we show that the witness of inseparability (entanglement), mutual information, quantum Fisher information, and the variance of cavity quadrature can be employed to detect the phase transition in quench. We also reveal the negative impact of temperature on checking the phase transition by quench. This model can be implemented using trapped ions, superconducting artificial atoms coupled bosonic modes, and quantum simulations. By reflecting the phase transition in a fundamental quantum optics model without imposing the thermodynamic limit, this work offers an idea to explore phase transitions by non-equilibrium process for open quantum systems.

quant-ph↗

arXiv2023

Real-time Signal Detection for Cyclotron Radiation Emission Spectroscopy Measurements using Antenna Arrays

Cyclotron Radiation Emission Spectroscopy (CRES) is a technique for precision measurement of the energies of charged particles, which is being developed by the Project 8 Collaboration to measure the neutrino mass using tritium beta-decay spectroscopy. Project 8 seeks to use the CRES technique to measure the neutrino mass with a sensitivity of 40~meV, requiring a large supply of tritium atoms stored in a multi-cubic meter detector volume. Antenna arrays are one potential technology compatible with an experiment of this scale, but the capability of an antenna-based CRES experiment to measure the neutrino mass depends on the efficiency of the signal detection algorithms. In this paper, we develop efficiency models for three signal detection algorithms and compare them using simulations from a prototype antenna-based CRES experiment as a case-study. The algorithms include a power threshold, a matched filter template bank, and a neural network based machine learning approach, which are analyzed in terms of their average detection efficiency and relative computational cost. It is found that significant improvements in detection efficiency and, therefore, neutrino mass sensitivity are achievable, with only a moderate increase in computation cost, by utilizing either the matched filter or machine learning approach in place of a power threshold, which is the baseline signal detection algorithm used in previous CRES experiments by Project 8.

physics.ins-det↗

arXiv2023

Mid-circuit qubit measurement and rearrangement in a $^{171}$Yb atomic array

Measurement-based quantum error correction relies on the ability to determine the state of a subset of qubits (ancillae) within a processor without revealing or disturbing the state of the remaining qubits. Among neutral-atom based platforms, a scalable, high-fidelity approach to mid-circuit measurement that retains the ancilla qubits in a state suitable for future operations has not yet been demonstrated. In this work, we perform imaging using a narrow-linewidth transition in an array of tweezer-confined $^{171}$Yb atoms to demonstrate nondestructive state-selective and site-selective detection. By applying site-specific light shifts, selected atoms within the array can be hidden from imaging light, which allows a subset of qubits to be measured while causing only percent-level errors on the remaining qubits. As a proof-of-principle demonstration of conditional operations based on the results of the mid-circuit measurements, and of our ability to reuse ancilla qubits, we perform conditional refilling of ancilla sites to correct for occasional atom loss, while maintaining the coherence of data qubits. Looking towards true continuous operation, we demonstrate loading of a magneto-optical trap with a minimal degree of qubit decoherence.

quant-ph↗

arXiv2023

First demonstration of 30 eVee ionization energy resolution with Ricochet germanium cryogenic bolometers

The future Ricochet experiment aims to search for new physics in the electroweak sector by measuring the Coherent Elastic Neutrino-Nucleus Scattering process from reactor antineutrinos with high precision down to the sub-100 eV nuclear recoil energy range. While the Ricochet collaboration is currently building the experimental setup at the reactor site, it is also finalizing the cryogenic detector arrays that will be integrated into the cryostat at the Institut Laue Langevin in early 2024. In this paper, we report on recent progress from the Ge cryogenic detector technology, called the CryoCube. More specifically, we present the first demonstration of a 30~eVee (electron equivalent) baseline ionization resolution (RMS) achieved with an early design of the detector assembly and its dedicated High Electron Mobility Transistor (HEMT) based front-end electronics. This represents an order of magnitude improvement over the best ionization resolutions obtained on similar heat-and-ionization germanium cryogenic detectors from the EDELWEISS and SuperCDMS dark matter experiments, and a factor of three improvement compared to the first fully-cryogenic HEMT-based preamplifier coupled to a CDMS-II germanium detector. Additionally, we discuss the implications of these results in the context of the future Ricochet experiment and its expected background mitigation performance.

astro-ph.IM↗

arXiv2023

Bayesian averaging for ground state masses of atomic nuclei in a Machine Learning approach

We present global predictions of the ground state mass of atomic nuclei based on a novel Machine Learning (ML) algorithm. We combine precision nuclear experimental measurements together with theoretical predictions of unmeasured nuclei. This hybrid data set is used to train a probabilistic neural network. In addition to training on this data, a physics-based loss function is employed to help refine the solutions. The resultant Bayesian averaged predictions have excellent performance compared to the testing set and come with well-quantified uncertainties which are critical for contemporary scientific applications. We assess extrapolations of the model's predictions and estimate the growth of uncertainties in the region far from measurements.

nucl-th↗