arXiv subjects

Qing Zhao

Publications and source records attributed to Qing Zhao.

At least 127 records · Page 7Linked to original sources

arXiv2022

MD simulations and experiments of plasma proteins adsorption to the biodegradable magnesium alloys to facilitate cell response

Once the magnesium alloy was implanted in the body, it was immediately covered with plasma proteins. The coated alloy surface promoted the adsorption and growth of osteoblasts. Herein, in vitro biological reactions of the ZK60 and AZ31 magnesium alloys were analyzed with and without plasma proteins incubation. The plasma proteins adsorbed on the magnesium alloy were characterized using mass spectrometry (MS). The MS results show that proteins related to bone cells such as fibrinogen, vitronectin, fibronectin, and prothrombin are prone to adsorbed on the surface of the alloys than other proteins. These proteins restrain the degradation of Mg alloys and promote the adsorption and growth of bone cells, which demonstrated by the immersion tests and biocompatibility assays. Furthermore, molecular dynamics simulations were used to analyze the details of the adsorptions of fibrinogen, fibronectin, and prothrombin on ZK60 and AZ31at atomic level. It is revealed that the type of residues adsorbed on the surface of the material has an important effect on protein adsorption.

physics.bio-ph↗

arXiv2022

Provably and Practically Efficient Neural Contextual Bandits

We consider the neural contextual bandit problem. In contrast to the existing work which primarily focuses on ReLU neural nets, we consider a general set of smooth activation functions. Under this more general setting, (i) we derive non-asymptotic error bounds on the difference between an overparameterized neural net and its corresponding neural tangent kernel, (ii) we propose an algorithm with a provably sublinear regret bound that is also efficient in the finite regime as demonstrated by empirical studies. The non-asymptotic error bounds may be of broader interest as a tool to establish the relation between the smoothness of the activation functions in neural contextual bandits and the smoothness of the kernels in kernel bandits.

stat.ML↗

arXiv2022

Robust quantum control for the manipulation of solid-state spins

Robust and high-fidelity control of electron spins in solids is the cornerstone for facilitating applications of solid-state spins in quantum information processing and quantum sensing. However, precise control of spin systems is always challenging due to the presence of a variety of noises originating from the environment and control fields. Herein, noise-resilient quantum gates, designed with robust optimal control (ROC) algorithms, are demonstrated experimentally with nitrogen-vacancy (NV) centers in diamond to realize tailored robustness against detunings and Rabi errors simultaneously. In the presence of both 10% off-resonant detuning and deviation of a Rabi frequency, we achieve an average single-qubit gate fidelity of up to 99.97%. Our experiments also show that, ROCbased multipulse quantum sensing sequences can suppress spurious responses resulting from finite widths and imperfections of microwave pulses, which provides an efficient strategy for enhancing the performance of existing multipulse quantum sensing sequences.

quant-ph↗

arXiv2022

Efficient measurement of the time-dependent cavity field through compressed sensing

We propose a method based on compressed sensing (CS) to measure the evolution processes of the states of a driven cavity quantum electrodynamics system. In precisely reconstructing the coherent cavity field amplitudes, we have to prepare the same states repetitively and each time perform one measurement with short sampling intervals considering the quantum nature of measurement and the Nyquist-Shannon sampling theorem. However, with the help of CS, the number of measurements can be exponentially reduced without loss of the recovery accuracy. We use largely detuned atoms and control their interactions with the cavity field to modulate coherent state amplitudes according to the scheme encoded in the sensing matrix. The simulation results show that the CS method efficiently recovers the amplitudes of the coherent cavity field even in the presence of noise.

quant-ph↗

arXiv2022

The Charging Performance of Su-Schrieffer-Heeger Quantum Battery

The Su-Schrieffer-Heeger (SSH) model has recently received considerable attention in condensed matter because it describes a typical one-dimensional system with topological edge states. Here, we investigate SSH-based charging protocols of quantum batteries (QB) with N quantum cells. This SSH QB hopping interaction induced ground state splitting makes the different effects of the dimerize parameter to the QB in the different quantum phase region. In the non-splitting region, the dimerize parameter has little influence on the QB. Whereas the fully-splitting region, the dimerize parameter has a significantly quantum advantage to the energy and ergotropy in the ground state fully splitting region, which leads the dimerize spin couples will have larger occupations than other spins. Although we have enhanced energy and ergotropy by the dimerize parameter, QB's capacity will decrease.

quant-ph↗

arXiv2022

Quantitative exploration of the absorber behavior of metal-insulator-metal metamaterials within terahertz via an asymmetric peak model

Terahertz (THz) metamaterials have been developed for THz sensing, detection, imaging, and many other functions due to their unusual absorbers. However, the unusual absorption spectra change with different incident angles. Thus, we designed and fabricated a focal plane array with metal-insulator-metal (MIM) structure metamaterial absorbers for further research. The absorption spectrum with incident angles from 20 to 60 was measured using THz time-domain spectroscopy (THz-TDS), and the experimental results reveal that the absorption spectrum changes with incident angle variations. A basic analytical asymmetric peak model for extracting absorption-frequency characteristics was developed in this study to quantitatively explore this variation in the absorber behavior with incident angles. The best result was that the frequency corresponding to the highest absorption can be easily found using this peak model. The experimental data was coherent with the validation of the asymmetric peak model. Moreover, a second model to quantitatively relate parameters to the incident angle was discovered, allowing for the prediction of absorption spectrum shifts and changes. The absorption spectrum was predicted to have a valley-like absorption curve at particular incident angles based on the secondary models deduction. The proposed extraction method's essential feature is that it can be applied to any physics-based MIM metamaterial system. Such a model will guide the design and optimization of THz metamaterial absorbers, sensors, imagers, and many others.

physics.optics↗

arXiv2022

Spectroscopy Approaches for Food Safety Applications: Improving Data Efficiency Using Active Learning and Semi-Supervised Learning

The past decade witnesses a rapid development in the measurement and monitoring technologies for food science. Among these technologies, spectroscopy has been widely used for the analysis of food quality, safety, and nutritional properties. Due to the complexity of food systems and the lack of comprehensive predictive models, rapid and simple measurements to predict complex properties in food systems are largely missing. Machine Learning (ML) has shown great potential to improve classification and prediction of these properties. However, the barriers to collect large datasets for ML applications still persists. In this paper, we explore different approaches of data annotation and model training to improve data efficiency for ML applications. Specifically, we leverage Active Learning (AL) and Semi-Supervised Learning (SSL) and investigate four approaches: baseline passive learning, AL, SSL, and a hybrid of AL and SSL. To evaluate these approaches, we collect two spectroscopy datasets: predicting plasma dosage and detecting foodborne pathogen. Our experimental results show that, compared to the de facto passive learning approach, AL and SSL methods reduce the number of labeled samples by 50% and 25% for each ML application, respectively.

cs.LG↗

arXiv2022

New many-body method using cluster expansion diagrams with tensor-optimized antisymmetrized molecular dynamics

We propose a new many-body method based on the correlation functions, in which the multiple products of the correlation functions are expanded into the many-body diagrams using the cluster expansion method and every diagram is independently optimized in the total-energy variation. We apply this idea to the tensor-optimized antisymmetrized molecular dynamics (TOAMD) using the bare nucleon-nucleon interaction and show the results of the $s$-shell nuclei within the triple products of the correlation functions of tensor and central-types. We evaluate the effect of the independent optimization of the many-body diagrams on the solutions. It is found that the triple products provides the sizable effect in the present scheme, which results in the good reproduction of the total energy and the Hamiltonian components of nuclei with respect to the few-body calculations.

nucl-th↗

arXiv2022

Enhanced quantum sensing with room-temperature solid-state masers

Quantum sensing with solid-state systems finds broad applications in diverse areas ranging from material and biomedical sciences to fundamental physics. Several solid-state spin sensors have been developed, facilitating the ultra-sensitive detection of physical quantities such as magnetic and electric fields and temperature. Exploiting collective behaviour of non-interacting spins holds the promise of pushing the detection limit to even lower levels, while to date, those levels are scarcely reached due to the broadened linewidth and inefficient readout of solid-state spin ensembles. Here, we experimentally demonstrate that such drawbacks can be overcome by newly reborn maser technology at room temperature in the solid state. Owing to maser action, we observe a 4-fold reduction in the inhomogeneously broadened linewidth of a molecular spin ensemble, which is narrower than the same measured from single spins at cryogenic temperatures. The maser-based readout applied to magnetometry showcases a signal-to-noise ratio (SNR) of 30 dB for single shots. This technique would be a significant addition to the toolbox for boosting the sensitivity of solid-state ensemble spin sensors.

quant-ph↗

arXiv2022

Ultralow-threshold green fluorescent protein laser based on high Q microbubble resonators

Biological lasers have attracted vast attention because of their potential medical application prospects, especially the low threshold biological laser, which can be used for ultrasensitive biological detection while ensuring that its luminous gain medium is not damaged by the high-energy pump light. By coupling the low concentration green fluorescent protein (GFP) solution with a high Q whispering gallery mode microbubble resonator, we managed to fabricate a miniature GFP laser with ultralow lasing threshold of 500 nJ/mm^2. The energy used to excite the GFP can be reduced to 380 fJ, two orders of magnitude lower than that of the lowest excitation energy GFP laser known. The Q value of the optical cavity in this biological laser is 5.3 x 10^7, the highest among GFP lasers at present. We further confirmed the long-term stability of the working characteristics of GFP laser for the first time and found that its optical characteristics can be maintained for at least 23 days. Finally, we measured the effects of different concentrations of fluorescent protein on the laser threshold. The data show that this biological laser can be used for a highly sensitive detection of GFP concentration.

physics.optics↗

arXiv2021

A Domain-Shrinking based Bayesian Optimization Algorithm with Order-Optimal Regret Performance

We consider sequential optimization of an unknown function in a reproducing kernel Hilbert space. We propose a Gaussian process-based algorithm and establish its order-optimal regret performance (up to a poly-logarithmic factor). This is the first GP-based algorithm with an order-optimal regret guarantee. The proposed algorithm is rooted in the methodology of domain shrinking realized through a sequence of tree-based region pruning and refining to concentrate queries in increasingly smaller high-performing regions of the function domain. The search for high-performing regions is localized and guided by an iterative estimation of the optimal function value to ensure both learning efficiency and computational efficiency. Compared with the prevailing GP-UCB family of algorithms, the proposed algorithm reduces computational complexity by a factor of $O(T^{2d-1})$ (where $T$ is the time horizon and $d$ the dimension of the function domain).

stat.ML↗

arXiv2021

As Easy as ABC: Adaptive Binning Coincidence Test for Uniformity Testing

We consider the problem of uniformity testing of Lipschitz continuous distributions with bounded support. The alternative hypothesis is a composite set of Lipschitz continuous distributions that are at least $\varepsilon$ away in $\ell_1$ distance from the uniform distribution. We propose a sequential test that adapts to the unknown distribution under the alternative hypothesis. Referred to as the Adaptive Binning Coincidence (ABC) test, the proposed strategy adapts in two ways. First, it partitions the set of alternative distributions into layers based on their distances to the uniform distribution. It then sequentially eliminates the alternative distributions layer by layer in decreasing distance to the uniform, and subsequently takes advantage of favorable situations of a distant alternative by exiting early. Second, it adapts, across layers of the alternative distributions, the resolution level of the discretization for computing the coincidence statistic. The farther away the layer is from the uniform, the coarser the discretization is needed for eliminating/exiting this layer. It thus exits both early in the detection process and quickly by using a lower resolution to take advantage of favorable alternative distributions. The ABC test builds on a novel sequential coincidence test for discrete distributions, which is of independent interest. We establish the sample complexity of the proposed tests as well as a lower bound.

math.ST↗

arXiv2021

Mid-wave infrared super-resolution imaging based on compressive calibration and sampling

Mid-wave infrared (MWIR) cameras for large number pixels are extremely expensive compared with their counterparts in visible light, thus, super-resolution imaging (SRI) for MWIR by increasing imaging pixels has always been a research hotspot in recent years. Over the last decade, with the extensively investigation of the compressed sensing (CS) method, focal plane array (FPA) based compressive imaging in MWIR developed rapidly for SRI. This paper presents a long-distance super-resolution FPA compressive imaging in MWIR with improved calibration method and imaging effect. By the use of CS, we measure and calculate the calibration matrix of optical system efficiently and precisely, which improves the imaging contrast and signal-to-noise ratio(SNR) compared with previous work. We also achieved the 4x4 times super-resolution reconstruction of the long-distance objects which reaches the limit of the system design in our experiment.

eess.IV↗

arXiv2021

Two-dimensional time- and space-resolved diagnostic method for integrated implosion process

To precisely measure and evaluate X-ray generation and evolution in a hohlraum during an implosion process, we present a two-dimensional (2D) time- and space-resolved diagnostic method by combining a compressed ultrafast photography (CUP) system and a simplified version of space-resolving flux detector (SSRFD). Numerical experiment results showed that the reconstruction quality of the conventional CUP significantly improved owing to the addition of the external SSRFD, especially when a coded mask with a large pixel size was used in the CUP. Further, the performance of the CUP cooperation with the SSRFD was better than that of adding an external charge-coupled device or streak camera. Compared with existing ultrafast imaging techniques in laser fusion, the proposed method has a prominent advantage of measuring the 2D evolution of implosion by combining high temporal resolution of streak camera and high spatial resolution of SSRFD; moreover, it can provide guidance for designing diagnostic experiments in laser fusion research.

physics.ins-det↗

arXiv2021

Anticorrosion and biocompatibility of a functionalized layer formed on ZK60 Mg alloy via hydroxyl ion implantation

Magnesium and its alloys have aroused tremendous interests because of their promising mechanical properties and biocompatibility. However, their excessively fast corrosion rate hinders the development of Mg alloys in the biomedical fields. Inspired by conventional ion implantation, a less-toxic functional group (hydroxyl) is used as the ion source to bombard the ZK60 Mg alloy surface to form a functionalized oxide layer. This functionalized oxide layer significantly facilitates the corrosion resistance of the ZK60 Mg alloy substrate and the proliferation of MC3T3-E1 cells, which is confirmed by electrochemical, immersion, and in vitro cytocompatibility tests. In comparison with results of ZK60 alloy implanted with carboxyl ions in our previous work, it is concluded that hydroxyl-treated alloys exhibit slightly higher corrosion rate while better biocompatibility. In summary, less-toxic functional ion implantation can be an effective strategy for inhibiting corrosion of Mg alloy implants and promoting their biocompatibility.

physics.app-ph↗

arXiv2021

Memory-Constrained No-Regret Learning in Adversarial Bandits

An adversarial bandit problem with memory constraints is studied where only the statistics of a subset of arms can be stored. A hierarchical learning policy that requires only a sublinear order of memory space in terms of the number of arms is developed. Its sublinear regret orders with respect to the time horizon are established for both weak regret and shifting regret. This work appears to be the first on memory-constrained bandit problems under the adversarial setting.

cs.LG↗

arXiv2021

Role of unitary correlation operator on high-momentum antisymmetrized molecular dynamics using bare NN interaction for 3H and 4He

We extend the high-momentum antisymmetrized molecular dynamics (HMAMD) by incorporating the short-range part of the unitary correlation operator method (UCOM) as the variational method of finite nuclei. In this HMAMD+UCOM calculation of light nuclei, the HMAMD is mainly in charge of the tensor correlation with up to the four-body correlation, while the short-range correlation is further improved by using the UCOM. The binding energies of the 3H and 4He nuclei are calculated with this HMAMD+UCOM using the AV8' bare nucleon-nucleon (NN) interaction. The different roles of the short-range and tensor correlations from the HMAMD and UCOM are analyzed in the numerical results. Compared with the previous calculations based on the different variational methods, this newly extended HMAMD+UCOM method can almost provide the consistent results with the ab initio results.

nucl-th↗

arXiv2021

Active Anomaly Detection with Switching Cost

The problem of detecting a single anomalous process among multiple independent processes is considered. Under a constraint on the number of processes that can be probed simultaneously, the decision maker should decide which processes to probe at each time and when to terminate the probing. Compared with previous work considering only the observation costs, the switching costs of switchings across processes also need to be taken into account in many practical scenarios. The objective is an active inference strategy that minimizes the Bayesian risk taking into account of the sample complexity, switching cost, as well as detection errors. Based on the framework of sequential design of experiments, we propose a low-complexity, low-switching deterministic policy for two scenarios where the total switching cost is negligible and the total switching cost is comparable to the total observation cost. We show that the proposed algorithm is asymptotically optimal in the former scenario and is order optimal in the latter scenario. Simulation results demonstrate strong performance in the finite regime for both scenarios.

eess.SP↗