arXiv subjects

Xuefeng Cui

Publications and source records attributed to Xuefeng Cui.

7 recordsLinked to original sources

arXiv2026

Coherent Floquet quantum reservoirs for molecular property prediction

Quantum reservoir computing (QRC) uses quantum dynamics to represent input histories for prediction through a trained classical readout. Discrete time crystals (DTCs) exhibit robust subharmonic responses under periodic driving, and previous work has used their dynamics to construct DTC-QRC. Here we construct a DTC-based reservoir architecture to predict molecular properties from structural and dynamical observations. Coherent Floquet evolution processes local molecular graph events and surface-hopping frames, while controlled reset regulates the contribution of earlier inputs. Measurements at the end of each input sequence yield a feature vector of fixed dimension. Trained classical decoders use this vector for inhibitor-activity and blood--brain-barrier permeability classification and electronic-gap forecasting, while the reservoir parameters remain fixed during training. With matched input lengths and output widths, DTC-QRC outperforms echo-state networks on long-prefix graph classification and the studied ethene gap forecasting tasks. Dephasing lowers performance in both applications, consistent with a role for coherent propagation. Experiments on the Quafu superconducting quantum cloud platform show that pair observables retain task information under device noise. The architecture provides a common framework for molecular screening and time-resolved property prediction using quantum reservoir computing.

quant-ph↗

arXiv2022

SHREC 2021: Classification in cryo-electron tomograms

Cryo-electron tomography (cryo-ET) is an imaging technique that allows three-dimensional visualization of macro-molecular assemblies under near-native conditions. Cryo-ET comes with a number of challenges, mainly low signal-to-noise and inability to obtain images from all angles. Computational methods are key to analyze cryo-electron tomograms. To promote innovation in computational methods, we generate a novel simulated dataset to benchmark different methods of localization and classification of biological macromolecules in tomograms. Our publicly available dataset contains ten tomographic reconstructions of simulated cell-like volumes. Each volume contains twelve different types of complexes, varying in size, function and structure. In this paper, we have evaluated seven different methods of finding and classifying proteins. Seven research groups present results obtained with learning-based methods and trained on the simulated dataset, as well as a baseline template matching (TM), a traditional method widely used in cryo-ET research. We show that learning-based approaches can achieve notably better localization and classification performance than TM. We also experimentally confirm that there is a negative relationship between particle size and performance for all methods.

eess.IV↗

arXiv2019

Magnetically-Sensitive Valley Polarization Reversal and Revival of Defect-Localized Excitons in WSe2-WS2

Manipulating and reserving the valley pseudospin of excitons is one core aim in the two-dimensional transition metal dichalcogenides (TMDs). However, due to the strong electron-hole exchange and spin-orbit coupling interactions, the exciton recombination lifetime is subject to picosecond timescale intrinsically, and the valley polarization is hardly modulated by a moderate magnetic field. It is fortunate that interlayer and defect-localized excitons promise to overcome these difficulties by suppressing the above interactions. Here we clearly reveal that the valley polarization can be reversed and revived in the defect-localized excitons with a microsecond lifetime in AB-stacked WSe2-WS2 heterobilayer. Specifically, for the interlayer defect-localized exciton, the valley polarization is reversed and can be efficiently enhanced by a weak out-of-plane magnetic field (<0.4 T). In sharp contrast, the valley polarization of the intralayer defect-localized exciton can revive after a fast decay process and follows the direction of the moderate out-of-plane magnetic field (<3 T). We explain the reversed valley polarization with highly magnetic sensitivity by the delocalization of defect-localized holes under a weak magnetic field and the revival of valley polarization by the valley Zeeman effect under a moderate magnetic field. Our results demonstrate that the valley pseudospin of defect-localized excitons can be efficiently modulated by the external magnetic field and enrich both the understanding and the technical approaches on manipulating the valley dynamics in TMDs and their heterostructure.

cond-mat.mtrl-sci↗

arXiv2016

When coding meets ranking: A joint framework based on local learning

Sparse coding, which represents a data point as a sparse reconstruction code with regard to a dictionary, has been a popular data representation method. Meanwhile, in database retrieval problems, learning the ranking scores from data points plays an important role. Up to now, these two problems have always been considered separately, assuming that data coding and ranking are two independent and irrelevant problems. However, is there any internal relationship between sparse coding and ranking score learning? If yes, how to explore and make use of this internal relationship? In this paper, we try to answer these questions by developing the first joint sparse coding and ranking score learning algorithm. To explore the local distribution in the sparse code space, and also to bridge coding and ranking problems, we assume that in the neighborhood of each data point, the ranking scores can be approximated from the corresponding sparse codes by a local linear function. By considering the local approximation error of ranking scores, the reconstruction error and sparsity of sparse coding, and the query information provided by the user, we construct a unified objective function for learning of sparse codes, the dictionary and ranking scores. We further develop an iterative algorithm to solve this optimization problem.

cs.CV↗

arXiv2014

Transient excitons at metal surfaces

Excitons, electron-hole pairs bound by the Coulomb potential, are fundamental quasiparticles of coherent light-matter interaction energizing processes from photosynthesis to optoelectronics. Excitons are observed in semiconductors, and their existence is implicit in the quantum theory of metals, yet their appearance is tenuous due to the screening of the Coulomb interaction on few femtosecond timescale. Here we present direct evidence for the dominant transient excitonic response at a Ag(111) surface, which precedes the full screening of the Coulomb interaction, in the course of a three-photon photoemission process with <15 femtosecond laser pulses. Electron-hole pair interaction through the excitonic response introduces coherent quasiparticle correlations beyond the single-particle description of the optics of metals, which dominate the multi-photon photoemission process.

cond-mat.mes-hall↗

arXiv2013

Towards Reliable Automatic Protein Structure Alignment

A variety of methods have been proposed for structure similarity calculation, which are called structure alignment or superposition. One major shortcoming in current structure alignment algorithms is in their inherent design, which is based on local structure similarity. In this work, we propose a method to incorporate global information in obtaining optimal alignments and superpositions. Our method, when applied to optimizing the TM-score and the GDT score, produces significantly better results than current state-of-the-art protein structure alignment tools. Specifically, if the highest TM-score found by TMalign is lower than (0.6) and the highest TM-score found by one of the tested methods is higher than (0.5), there is a probability of (42%) that TMalign failed to find TM-scores higher than (0.5), while the same probability is reduced to (2%) if our method is used. This could significantly improve the accuracy of fold detection if the cutoff TM-score of (0.5) is used. In addition, existing structure alignment algorithms focus on structure similarity alone and simply ignore other important similarities, such as sequence similarity. Our approach has the capacity to incorporate multiple similarities into the scoring function. Results show that sequence similarity aids in finding high quality protein structure alignments that are more consistent with eye-examined alignments in HOMSTRAD. Even when structure similarity itself fails to find alignments with any consistency with eye-examined alignments, our method remains capable of finding alignments highly similar to, or even identical to, eye-examined alignments.

q-bio.QM↗

arXiv2006

Response-table-based virtual responding method and instrument and their applications in scanning probe microscopes

We describe a virtual response method and device. It consists of an analog-to-digital converter, a digital-to-analog converter, and a computer and utilizes a searchable response table (RT) pre-stored in the computer to respond to electronic signals. The RT is constructed by measuring the input-output relationship of a real response machine followed by sorting it per input data. To respond, incoming signal is converted to digital data whose position in the RT is then located. The response signal is determined by localized numerical calculation around that position. This method has many advantages: cheap, fast, universal, stable with less noises and errors.

physics.ins-det↗