BZPEERPapers, people, evidence and research paths
Menu

Discover

SearchFind papers, people, topics, institutions and opportunities from one place.GraphSee how papers, authors, topics and evidence connect.

Research tools

HomeContinue from the papers, people and topics that matter now.CollectionsTurn saved papers into reading lists, evidence maps and shareable context.ResearchReconstruct the path that led you from search to paper to question.CollaborationMove from a paper, topic or expert request into a focused thread.PublishCreate full-text research articles with metadata, formulas and versions.

Network

ExpertsFind specialists whose work and reviews explain why they can help.CommunitiesJoin research groups organized around papers, topics and shared evidence.

Opportunities

ListingsBrowse roles, calls and collaborations connected to real research context.

Account

Log inReturn to your saved papers, graph views and active threads.Create accountStart saving papers, building a research trail and joining teams.
Log inCreate account

Source author record

Pei Zhang

Pei Zhang appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

ResearcherUnclaimed source record
quant-phphysics.opticsComputer VisionMachine Learningeess.IVArtificial IntelligenceComputation and Languagephysics.acc-phphysics.atom-phphysics.comp-phcond-mat.dis-nncond-mat.mtrl-scicond-mat.softDistributed, Parallel, and Cluster ComputingGenomicsNeural and Evolutionary Computingphysics.chem-phphysics.ins-detphysics.plasm-phRobotics

Catalog footprint

What is connected

43works
20topics
4close collaborators

Actions

Connect this record

Log in to claim
Open graphBrowse works

Research graph

See the researcher in context

Open full explorer

Inspect adjacent papers, topics, institutions and collaborators without losing the researcher page.

Building this map preview

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Published work

43 published item(s)

preprint2026arXiv

Aquatic Neuromorphic Optical Flow

Underwater environments impose severe constraints on conventional imaging systems and demand solutions that balance high-quality sensing with strict resource efficiency. While emerging event cameras offer a promising alternative, their potential in aquatic scenarios remains largely unexplored. Through the lens of neuromorphic vision, this work pioneers the investigation of motion fields that serve as key media for agile underwater perception. Built upon spiking neural networks, we introduce a self-supervised framework to estimate per-pixel optical flow from asynchronous event streams, elegantly bypassing the long-standing bottleneck of underwater data scarcity. Extensive evaluations demonstrate that our method achieves competitive visual and quantitative results against leading techniques while operating with superior computational efficiency. By bridging neuromorphic sensing and aquatic intelligence, this work opens new frontiers for lightweight, real-time, and low-cost perception on resource-constrained underwater edge platforms.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

LISA: Language-guided Interference-aware Spatial-Frequency Attention for Driver Gaze Estimation

Driver gaze estimation serves as a fundamental metric for evaluating driver attentiveness in modern monitoring systems. Beyond being vulnerable to sudden lighting changes and sensor noise, spatial-domain models struggle to disentangle authentic gaze cues from irrelevant visual attributes. In this paper, we propose LISA, a \textbf{L}anguage-guided \textbf{I}nterference-aware \textbf{S}patial-Frequency \textbf{A}ttention framework that combines frequency-domain priors with vision-language knowledge. Observing that the amplitude spectrum remains relatively stable even under spatial perturbations, we design a dual-domain fusion mechanism. It integrates stable low-frequency semantics into high-frequency details, employing spatial attention to precisely target ocular regions. To reduce semantic ambiguity, we also introduce a training-time disentanglement strategy. Using a frozen CLIP encoder and orthogonal regularization, we explicitly separate gaze features from appearance interference. Experiments on two benchmarks show that LISA achieves state-of-the-art performance, with significantly improved robustness against occlusions and lighting variations. The code repository is available at https://github.com/Mason-bupt/LISA.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

See Silhouettes in Motion with Neuromorphic Vision

Quasi-bimodal objects, such as text, road signs, and barcodes, play a basic yet vital role in daily visual communication. By boiling these down to clear silhouettes, binarization uses a minimal language to convey essential vision cues for maximum downstream efficiency. The catch is that frame-based imaging often struggles on mobile platforms like drones, self-driving cars, and underwater vehicles. In these dynamic scenes, rapid motion and harsh lighting can make it blind, causing severe motion blur and erasing crucial details. To overcome the limits, neuromorphic vision via event cameras, featuring microsecond-level temporal resolution and high dynamic range, steps in as a natural solution. Building upon this event-driven sensing paradigm, we introduce a simple yet effective dual-modal approach that harnesses the synergy between frames and events to achieve real-time, high-frame-rate binarization on CPU-only devices. Extensive evaluations present that it earns competitive performance against leading techniques in reducing motion blur, while delivering impressive improvements under challenging illumination. Besides, our asynchronous workflow bypasses event scarcity that breaks traditional time-binning reconstruction, maintaining clear target shapes even at extreme kilohertz frame rates. Its binary results further serve as reliable representations that facilitate a range of downstream tasks. This work paves the way towards lightweight perception and interaction in embodied intelligence on resource-constrained edge platforms.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Autoregressive long-horizon prediction of plasma edge dynamics

Accurate modeling of scrape-off layer (SOL) and divertor-edge dynamics is vital for designing plasma-facing components in fusion devices. High-fidelity edge fluid/neutral codes such as SOLPS-ITER capture SOL physics with high accuracy, but their computational cost limits broad parameter scans and long transient studies. We present transformer-based, autoregressive surrogates for efficient prediction of 2D, time-dependent plasma edge state fields. Trained on SOLPS-ITER spatiotemporal data, the surrogates forecast electron temperature, electron density, and radiated power over extended horizons. We evaluate model variants trained with increasing autoregressive horizons (1-100 steps) on short- and long-horizon prediction tasks. Longer-horizon training systematically improves rollout stability and mitigates error accumulation, enabling stable predictions over hundreds to thousands of steps and reproducing key dynamical features such as the motion of high-radiation regions. Measured end-to-end wall-clock times show the surrogate is orders of magnitude faster than SOLPS-ITER, enabling rapid parameter exploration. Prediction accuracy degrades when the surrogate enters physical regimes not represented in the training dataset, motivating future work on data enrichment and physics-informed constraints. Overall, this approach provides a fast, accurate surrogate for computationally intensive plasma edge simulations, supporting rapid scenario exploration, control-oriented studies, and progress toward real-time applications in fusion devices.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Direct Light Orbital Angular Momentum Detection in Mid-Infrared based on Type-II Weyl Semimetal TaIrTe4

The capability of direct photocurrent detection of orbital angular momentum (OAM) of light has recently been realized with topological Weyl semimetal, but limited to near infrared wavelength range. The extension of direct OAM detection to midinfrared, a wavelength range that plays important role in a vast range of applications, such as autonomous driving, night vision and motion detection, is challenging and has not yet been realized. This is because most studies of photocurrent responses are not sensitive to the phase information and the photo response is usually very poor in the mid-infrared. In this study, we designed a photodetector based on Type-II Weyl semimetal tantalum iridium tellurides with designed electrode geometries for direct detection of the topological charge of OAM through orbital photogalvanic effect. Our results indicate helical phase gradient of light can be distinguished by a current winding around the optical beam axis with a magnitude proportional to its quantized OAM mode number. The topological enhanced response at mid-infrared of TaIrTe4 further help overcome the low responsivity issues and finally render the direct orbital angular momentum detection capability in mid-infrared. Our study enables on-chip integrated OAM detection, and thus OAM sensitive focal plane arrays in mid-infrared. Such capability triggers new route to explore applications of light carrying OAM, especially that it can crucially promote the performance of many mid-infrared imaging related applications, such as intricate target recognition and night vision.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Friction-dependent rheology of dry granular systems

Understanding the rheology of granular assemblies is important for natural and engineering systems, but the relationship between inter-particle friction (or microscopic friction) and macroscopic friction is still not well understood. In this study, using the the discrete element method (DEM) with spherical particles and realistic contact laws, we investigate the mechanics of granular systems with a wide range of inter-particle frictional coefficients and aim to establish a friction-dependent rheology for dry granular flows. The corresponding results show that increasing inter-particle friction dramatically increases the effective frictional coefficient, $μ_{\textrm{eff}}$, while decreasing the solid fraction of the system and increasing the transitional inertial number that marks the division of quasi-static regimes and intermediate flow regimes. We further propose a new dimensionless number, $\mathcal{M}$, as a ratio between the inertial effect and frictional effect, which is similar to the effective aspect ratio in granular column collapses, and unifies the influence of inter-particle friction with the inertial number. We then establish a relationship between $\mathcal{M}$ and the dimensionless granular temperature, $Θ$, to further universalize the influence of inter-particle frictions. Such study can broaden the application of the $μ(I)$ rheology in natural and engineering systems and help establish a more general constitutive model for complex granular systems.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems

We introduce a multi-tasking graph convolutional neural network, HydraGNN, to simultaneously predict both global and atomic physical properties and demonstrate with ferromagnetic materials. We train HydraGNN on an open-source ab initio density functional theory (DFT) dataset for iron-platinum (FePt) with a fixed body centered tetragonal (BCT) lattice structure and fixed volume to simultaneously predict the mixing enthalpy (a global feature of the system), the atomic charge transfer, and the atomic magnetic moment across configurations that span the entire compositional range. By taking advantage of underlying physical correlations between material properties, multi-task learning (MTL) with HydraGNN provides effective training even with modest amounts of data. Moreover, this is achieved with just one architecture instead of three, as required by single-task learning (STL). The first convolutional layers of the HydraGNN architecture are shared by all learning tasks and extract features common to all material properties. The following layers discriminate the features of the different properties, the results of which are fed to the separate heads of the final layer to produce predictions. Numerical results show that HydraGNN effectively captures the relation between the configurational entropy and the material properties over the entire compositional range. Overall, the accuracy of simultaneous MTL predictions is comparable to the accuracy of the STL predictions. In addition, the computational cost of training HydraGNN for MTL is much lower than the original DFT calculations and also lower than training separate STL models for each property.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Multiparameter simultaneous optimal estimation with an SU(2) coding unitary evolution

In a ubiquitous $SU(2)$ dynamics, achieving the simultaneous optimal estimation of multiple parameters is significant but difficult. Using quantum control to optimize this $SU(2)$ coding unitary evolution is one of solutions. We propose a method, characterized by the nested cross-products of the coefficient vector $\mathbf{X}$ of $SU(2)$ generators and its partial derivative $\partial_\ell \mathbf{X}$, to investigate the control-enhanced quantum multiparameter estimation. Our work reveals that quantum control is not always functional in improving the estimation precision, which depends on the characterization of an $SU(2)$ dynamics with respect to the objective parameter. This characterization is quantified by the angle $α_\ell$ between $\mathbf{X}$ and $\partial_\ell \mathbf{X}$. For an $SU(2)$ dynamics featured by $α_\ell=π/2$, the promotion of the estimation precision can get the most benefits from the controls. When $α_\ell$ gradually closes to $0$ or $π$, the precision promotion contributed to by quantum control correspondingly becomes inconspicuous. Until a dynamics with $α_\ell=0$ or $π$, quantum control completely loses its advantage. In addition, we find a set of conditions restricting the simultaneous optimal estimation of all the parameters, but fortunately, which can be removed by using a maximally entangled two-qubit state as the probe state and adding an ancillary channel into the configuration. Lastly, a spin-$1/2$ system is taken as an example to verify the above-mentioned conclusions. Our proposal sufficiently exhibits the hallmark of control-enhancement in fulfilling the multiparameter estimation mission, and it is applicable to an arbitrary $SU(2)$ parametrization process.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

PI3NN: Out-of-distribution-aware prediction intervals from three neural networks

We propose a novel prediction interval (PI) method for uncertainty quantification, which addresses three major issues with the state-of-the-art PI methods. First, existing PI methods require retraining of neural networks (NNs) for every given confidence level and suffer from the crossing issue in calculating multiple PIs. Second, they usually rely on customized loss functions with extra sensitive hyperparameters for which fine tuning is required to achieve a well-calibrated PI. Third, they usually underestimate uncertainties of out-of-distribution (OOD) samples leading to over-confident PIs. Our PI3NN method calculates PIs from linear combinations of three NNs, each of which is independently trained using the standard mean squared error loss. The coefficients of the linear combinations are computed using root-finding algorithms to ensure tight PIs for a given confidence level. We theoretically prove that PI3NN can calculate PIs for a series of confidence levels without retraining NNs and it completely avoids the crossing issue. Additionally, PI3NN does not introduce any unusual hyperparameters resulting in a stable performance. Furthermore, we address OOD identification challenge by introducing an initialization scheme which provides reasonably larger PIs of the OOD samples than those of the in-distribution samples. Benchmark and real-world experiments show that our method outperforms several state-of-the-art approaches with respect to predictive uncertainty quality, robustness, and OOD samples identification.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Retrieving High-Dimensional Quantum Steering From a Noisy Environment with N Measurement Settings

One of the most often implied benefits of high-dimensional (HD) quantum systems is to lead to stronger forms of correlations, featuring increased robustness to noise. Here, we experimentally demonstrate the $n$-setting linear HD quantum steering criterion. We verify the large violation of the steering inequalities without full-state tomography. The lower bound of the violation is $2.24\pm0.01$ in 11 dimensions, exceeding the bound ($V<2$) of 2-setting criteria. Hence, a higher strength of steering has been revealed. Moreover, we demonstrate the method for enhancing the noise robustness without increasing dimension, alternatively, by increasing measurement settings. Using the entanglement in 11 dimensions, we experimentally retrieve steering nonlocality with $63.4\pm1.4\%$ isotropic noise fraction, surpassing the $50\%$ limitation of 2-setting criteria. Our work offers the potential for practical one-sided device-independent quantum information processing that tolerates the noisy environment, lossy detection, and transcends the present transmission distance limitation.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules

Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the graph representation of molecular structures. Training an accurate and comprehensive GCNN surrogate for molecular design requires large-scale graph datasets and is usually a time-consuming process. Recent advances in GPUs and distributed computing open a path to reduce the computational cost for GCNN training effectively. However, efficient utilization of high performance computing (HPC) resources for training requires simultaneously optimizing large-scale data management and scalable stochastic batched optimization techniques. In this work, we focus on building GCNN models on HPC systems to predict material properties of millions of molecules. We use HydraGNN, our in-house library for large-scale GCNN training, leveraging distributed data parallelism in PyTorch. We use ADIOS, a high-performance data management framework for efficient storage and reading of large molecular graph data. We perform parallel training on two open-source large-scale graph datasets to build a GCNN predictor for an important quantum property known as the HOMO-LUMO gap. We measure the scalability, accuracy, and convergence of our approach on two DOE supercomputers: the Summit supercomputer at the Oak Ridge Leadership Computing Facility (OLCF) and the Perlmutter system at the National Energy Research Scientific Computing Center (NERSC). We present our experimental results with HydraGNN showing i) reduction of data loading time up to 4.2 times compared with a conventional method and ii) linear scaling performance for training up to 1,024 GPUs on both Summit and Perlmutter.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Verification of Kochen-Specker-type quantum contextuality with a single photon

Contextuality provides one of the fundamental characterizations of quantum phenomena, and can be used as a resource in lots of quantum information processing. In this paper, we summarize and derive some equivalent noncontextual inequalities from different noncontextual models of the proofs for Kochen-Specker theorem based on Greenberger-Horne-Zeilinger states. These noncontextual inequalities are equivalent up to some correlation items which hold both for noncontextual hidden variable theories and quantum mechanics. Therefore, using single-photon hyperentangled Greenberger-Horne-Zeilinger states encoded by spin, path and orbital angular momentum, we experimentally verify several state-dependent noncontextual models of the proofs for the Kochen-Specker theorem by testing an extreme simplest Mermin-like inequality.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

PSA: A novel optimization algorithm based on survival rules of porcellio scaber

Bio-inspired algorithms such as neural network algorithms and genetic algorithms have received a significant amount of attention in both academic and engineering societies. In this paper, based on the observation of two major survival rules of a species of woodlice, i.e., porcellio scaber, we present an algorithm called the porcellio scaber algorithm (PSA) for solving general unconstrained optimization problems, including differentiable and non-differential ones as well as the case with local optima. Numerical results based on benchmark problems are presented to validate the efficacy of PSA.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Realization of a deterministic quantum Toffoli gate with a single photon

Quantum controlled-logic gates, including controlled NOT gate and Toffoli gate, play critical roles in lots of quantum information processing schemes. We design and experimentally demonstrate deterministic Toffoli gate by utilizing orbital-angular-momentum and polarization degrees of freedom of a single photon. In addition, we generate Bell states by using the controlled NOT gate. The effective conversion rate of the Toffoli gate in our experiment is $(95.1\pm3.2)\%$. Furthermore, our experimental setup does not require any auxiliary photons and probabilistic post selections.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Extending Label Smoothing Regularization with Self-Knowledge Distillation

Inspired by the strong correlation between the Label Smoothing Regularization(LSR) and Knowledge distillation(KD), we propose an algorithm LsrKD for training boost by extending the LSR method to the KD regime and applying a softer temperature. Then we improve the LsrKD by a Teacher Correction(TC) method, which manually sets a constant larger proportion for the right class in the uniform distribution teacher. To further improve the performance of LsrKD, we develop a self-distillation method named Memory-replay Knowledge Distillation (MrKD) that provides a knowledgeable teacher to replace the uniform distribution one in LsrKD. The MrKD method penalizes the KD loss between the current model's output distributions and its copies' on the training trajectory. By preventing the model learning so far from its historical output distribution space, MrKD can stabilize the learning and find a more robust minimum. Our experiments show that LsrKD can improve LSR performance consistently at no cost, especially on several deep neural networks where LSR is ineffectual. Also, MrKD can significantly improve single model training. The experiment results confirm that the TC can help LsrKD and MrKD to boost training, especially on the networks they are failed. Overall, LsrKD, MrKD, and their TC variants are comparable to or outperform the LSR method, suggesting the broad applicability of these KD methods.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

LaNet: Real-time Lane Identification by Learning Road SurfaceCharacteristics from Accelerometer Data

The resolution of GPS measurements, especially in urban areas, is insufficient for identifying a vehicle's lane. In this work, we develop a deep LSTM neural network model LaNet that determines the lane vehicles are on by periodically classifying accelerometer samples collected by vehicles as they drive in real time. Our key finding is that even adjacent patches of road surfaces contain characteristics that are sufficiently unique to differentiate between lanes, i.e., roads inherently exhibit differing bumps, cracks, potholes, and surface unevenness. Cars can capture this road surface information as they drive using inexpensive, easy-to-install accelerometers that increasingly come fitted in cars and can be accessed via the CAN-bus. We collect an aggregate of 60 km driving data and synthesize more based on this that capture factors such as variable driving speed, vehicle suspensions, and accelerometer noise. Our formulated LSTM-based deep learning model, LaNet, learns lane-specific sequences of road surface events (bumps, cracks etc.) and yields 100% lane classification accuracy with 200 meters of driving data, achieving over 90% with just 100 m (correspondingly to roughly one minute of driving). We design the LaNet model to be practical for use in real-time lane classification and show with extensive experiments that LaNet yields high classification accuracy even on smooth roads, on large multi-lane roads, and on drives with frequent lane changes. Since different road surfaces have different inherent characteristics or entropy, we excavate our neural network model and discover a mechanism to easily characterize the achievable classification accuracies in a road over various driving distances by training the model just once. We present LaNet as a low-cost, easily deployable and highly accurate way to achieve fine-grained lane identification.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Learning Contextualized Sentence Representations for Document-Level Neural Machine Translation

Document-level machine translation incorporates inter-sentential dependencies into the translation of a source sentence. In this paper, we propose a new framework to model cross-sentence dependencies by training neural machine translation (NMT) to predict both the target translation and surrounding sentences of a source sentence. By enforcing the NMT model to predict source context, we want the model to learn "contextualized" source sentence representations that capture document-level dependencies on the source side. We further propose two different methods to learn and integrate such contextualized sentence embeddings into NMT: a joint training method that jointly trains an NMT model with the source context prediction model and a pre-training & fine-tuning method that pretrains the source context prediction model on a large-scale monolingual document corpus and then fine-tunes it with the NMT model. Experiments on Chinese-English and English-German translation show that both methods can substantially improve the translation quality over a strong document-level Transformer baseline.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Long-Short Term Masking Transformer: A Simple but Effective Baseline for Document-level Neural Machine Translation

Many document-level neural machine translation (NMT) systems have explored the utility of context-aware architecture, usually requiring an increasing number of parameters and computational complexity. However, few attention is paid to the baseline model. In this paper, we research extensively the pros and cons of the standard transformer in document-level translation, and find that the auto-regressive property can simultaneously bring both the advantage of the consistency and the disadvantage of error accumulation. Therefore, we propose a surprisingly simple long-short term masking self-attention on top of the standard transformer to both effectively capture the long-range dependence and reduce the propagation of errors. We examine our approach on the two publicly available document-level datasets. We can achieve a strong result in BLEU and capture discourse phenomena.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

O-MedAL: Online Active Deep Learning for Medical Image Analysis

Active Learning methods create an optimized labeled training set from unlabeled data. We introduce a novel Online Active Deep Learning method for Medical Image Analysis. We extend our MedAL active learning framework to present new results in this paper. Our novel sampling method queries the unlabeled examples that maximize the average distance to all training set examples. Our online method enhances performance of its underlying baseline deep network. These novelties contribute significant performance improvements, including improving the model's underlying deep network accuracy by 6.30%, using only 25% of the labeled dataset to achieve baseline accuracy, reducing backpropagated images during training by as much as 67%, and demonstrating robustness to class imbalance in binary and multi-class tasks.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Measuring mode indices of a partially coherent vortex beam with HBT type experiment

It is known that the cross-correlation function (CCF) of a partially coherent vortex (PCV) beam shows a robust link with the radial and azimuthal mode indices. However, the previous proposals are difficult to measure the CCF in practical system, especially in the case of astronomical objects. In this letter, we demonstrate experimentally that the Hanbury Brown and Twiss effect can be used to measure the mode indices of the original vortex beam and investigate the relationship between the spatial coherent width and the characterization of CCF of a PCV beam. The technique we exploit is quite efficient and robust, and it may be useful in the field of free space communication and astronomy which are related to the photon's orbital angular momentum.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Observing spin Hall effect of pseudo-thermal light through weak measurement

It is well known that the spin Hall effect of light (SHEL) can be easily observed via weak measurement by the dark strip resulted from the splitting of different polarization components. We find that the SHEL of partially coherent beam (PCB) also has similar phenomenon. However, the existence of the dark strip in the SHEL of PCB can not be properly justified by considering the beam as a statistical assemble of coherent speckles. Also, the dark strip of PCB is not purely dark. By analyzing the autocorrelation of the central dark strip part of the SHEL of PCB, we show that SHEL of PCB is essentially the direct result of overlapping coherent speckles' SHEL. Then we further prove our conclusion by adjusting the converging level and the incident angle of the beam. Finally, we develop a qualitative theory by taking the intensity distribution of PCB into account, to fully clarify the SHEL of PCB. The feasibility of our theory to Goos-Hänchen (GH) scenario is also demonstrated.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Orbital angular momentum complex spectrum analyzer for vortex light based on rotational Doppler Effect

The function to measure orbital angular momentum (OAM) distribution of vortex light is essential for OAM applications. Although there are lots of works to measure OAM modes, it is difficult to measure the power distribution of different OAM modes quantitatively and instantaneously, let alone measure the phase distribution among them. In this work, we demonstrate an OAM complex spectrum analyzer, which enables to measure the power and phase distribution of OAM modes simultaneously by employing rotational Doppler Effect. The original OAM mode distribution is mapped to electrical spectrum of beating signals with a photodetector. The power distribution and phase distribution of superimposed OAM beams are successfully retrieved by analyzing the electrical spectrum. We also extend the measurement to other spatial modes, such as linear polarization modes. These results represent a new landmark of spatial mode analysis and show great potentials in optical communication and OAM quantum state tomography.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Spatial sub-Rayleigh imaging analysis via speckle laser illumination

It is commonly accepted that optical sub-Rayleigh imaging has potential application in many fields. In this Letter, by confining the divergence of the optical field, as well as the size of the illumination source, we show that the first-order averaged intensity measurement via speckle laser illumina- tion can make an actual breakthrough on the Rayleigh limit. For a high-order algorithm, it has been reported that the autocorrelation function can be utilized to achieve the sub-Rayleigh feature. However, we find that this sub- Rayleigh feature for the high-order algorithm is limited only to binary objects, and the image will be distorted when a gray object is placed. This property encourages us to find the physics behind the high-order correlation imaging algo- rithm. We address these explanations in this Letter and find that for different types of high-order algorithm, there is always a seat in the right place from the cross-correlation function.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Theoretical analysis and experimental verification on optical rotational Doppler effect

We present a theoretical model to sufficiently investigate the optical rotational Doppler effect based on modal expansion method. We find that the frequency shift content is only determined by the surface of spinning object and the reduced Doppler shift is linear to the difference of mode index between input and output orbital angular momentum (OAM) light, and linear to the rotating speed of spinning object as well. An experiment is carried out to verify the theoretical model. We explicitly suggest that the spatial spiral phase distribution of spinning object determines the frequency content. The theoretical model makes us better understand the physical processes of rotational Doppler effect, and thus has many related application fields, such as detection of rotating bodies, imaging of surface and measurement of OAM light.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Analysis on rotational Doppler Effect based on modal expansion method

We theoretically investigate the optical rotational Doppler Effect using modal expansion method. We find that the frequency shift content is only determined by the surface of spinning object and the reduced Doppler shift is linear to the change of mode index. The theoretical model makes us better understand the physical processes of rotational Doppler Effect. It can provide theoretical guidance for many related applications, such as detection of rotating bodies, detection of OAM and frequency shift.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Demonstration of quantum permutation algorithm with a single photon ququart

We report an experiment to demonstrate a quantum permutation determining algorithm with linear optical system. By employing photon polarization and spatial modes, we realize the quantum ququart states and all the essential permutation transformations. Compared with the classical case, this work determines the parity of the permutation in only one step of evaluation and displays the remarkable speedup of quantum algorithm. This experiment is accomplished in single photon level and exhibits strong universality in quantum computation.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Probing the topological charge of a vortex beam with dynamic angular double slits

When a vortex beam with the spiral phase structure passes through a dynamic angular double slits (ADS), the interference pattern changes alternatively between destructive and constructive at the angular bisector direction of the ADS due to their phase difference. Based on this property, we experimentally demonstrate a simple method, which can precisely and efficiently determine the topological charge of vortex beams. What is more, this scheme allows determining both the modulus and sign of the topological charge of vortex beams simultaneously.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Super sub-wavelength patterns in photon coincidence detection

High-precision measurements implemented by means of light is desired in all fields of science. However, light is a wave and Rayleigh criterion gives us a diffraction limitation in classical optics which restricts to get arbitrary high resolution. Sub-wavelength interference has a potential application in lithography to beat the classical Rayleigh limit of resolution. We carefully study the second-order correlation theory to get the physics behind sub-wavelength interference in photon coincidence detection. A Young's double-slit experiment with pseudo-thermal light is carried out to test the second-order correlation pattern. The result shows that when different scanning ways of two point detectors are chosen, one can get super sub-wavelength interference patterns. We then give a theoretical explanation to this surprising result, and find this explanation is also suitable for the result by using entangled light. Furthermore, we discuss the limitation of this kind of super sub-wavelength interference patterns in quantum lithography.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

DNA methylation variation in Arabidopsis has a genetic basis and shows evidence of local adaptation

Epigenome modulation in response to the environment potentially provides a mechanism for organisms to adapt, both within and between generations. However, neither the extent to which this occurs, nor the molecular mechanisms involved are known. Here we investigate DNA methylation variation in Swedish Arabidopsis thaliana accessions grown at two different temperatures. Environmental effects on DNA methylation were limited to transposons, where CHH methylation was found to increase with temperature. Genome-wide association mapping revealed that the extensive CHH methylation variation was strongly associated with genetic variants in both cis and trans, including a major trans-association close to the DNA methyltransferase CMT2. Unlike CHH methylation, CpG gene body methylation (GBM) on the coding region of genes was not affected by growth temperature, but was instead strongly correlated with the latitude of origin. Accessions from colder regions had higher levels of GBM for a significant fraction of the genome, and this was correlated with elevated transcription levels for the genes affected. Genome-wide association mapping revealed that this effect was largely due to trans-acting loci, a significant fraction of which showed evidence of local adaptation. These findings constitute the first direct link between DNA methylation and adaptation to the environment, and provide a basis for further dissecting how environmentally driven and genetically determined epigenetic variation interact and influence organismal fitness.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Quantum gambling based on Nash-equilibrium

A fair gambling is hard to be made between two spatially separated parties without introducing a trusted third party. Here we propose a novel gambling protocol, which enables fair gambling between two distant parties without the help of a third party. By incorporating the key concepts and methods of game theory, our protocol will force the two parties to move their strategies to a Nash-equilibrium point which guarantees the fairness through the physical laws of quantum mechanics. Furthermore, we show that our protocol can be easily adapted to a biased version, which would find applications in lottery, casino, etc. A proof-of-principle optical demonstration of this protocol is reported as well.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Characterizing vortex beam with angular-double-slit interference

The Fraunhofer diffraction intensity distribution of Laguerre-Gaussian beam is studied in an angular-double-slit interferometer. We demonstrate that the spiral phase structure of vortex light can be clearly revealed in this interference geometry, and it gives us an efficient way to distinguish different order of Laguerre-Gaussian beams. This angular-double-slit interference gives us a better understanding to the nature of orbital angular momentum and the interpretation of vortex beams interference phenomenon.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Control excitation and coherent transfer in a dimer

In this article, the processes of energy absorption and coherent transfer in a dimer is studied. The dimer includes two two-level pigments --- donor and acceptor, where donor is assumed being excited by a control pulse in the time domain. We investigate the dynamics of probability that the acceptor is in the excited state and the total efficiency of energy absorption and transfer under different temporal shape of control pulse. Quantum concurrence of the dimer is also discussed.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Evaluating Laguerre-Gaussian beams with an invariant parameter

We define a new parameter about Laguerre-Gaussian (LG) beams, named $Q^{l}_{p}$, which is only related to mode indices $p$ and $l$. This parameter is able to both evaluate and distinguish LG beams. The $Q^{l}_{p}$ values are first calculated theoretically and then measured experimentally for several different LG beams. Another mode quality parameter, $ M^{2} $ value, is also measured. The comparison between $Q^{l}_{p}$ and $ M^{2}$ shows same trend for the quality of LG mode, while the measurement of $Q^{l}_{p}$ is much easier than $ M^{2}$.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Deutsch's algorithm with topological charges of optical vortices via non-degenerate four-wave mixing

We propose a scheme to implement the Deutsch's algorithm through non-degenerate four-wave mixing process. By employing photon topological charges of optical vortices, we demonstrate the ability to realize the necessary four logic gates for all balanced and constant functions. We also analyze the feasibility of the proposed scheme on the single photon level.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Higher order mode spectra and the dependence of localized dipole modes on the transverse beam position in third harmonic superconducting cavities at FLASH

An electron beam entering an accelerating cavity excites a wakefield. This wakefield can be decomposed into a series of multi-poles or modes. The dominant component of the transverse wakefield is dipole. This report summarizes the higher order mode (HOM) signals of the third harmonic cavities of FLASH measured at various stages: transmission measurements in the single cavity test stand at Fermilab, at CMTB (Cryo-Module Test Bench) and at FLASH, and beam-excited measurements at FLASH. Modes in the first two dipole bands and the fifth dipole band have been identified using a global Lorentzian fit technique. The beam-pipe modes at approximately 4 GHz and some modes in the fifth dipole band have been observed as localized modes, while the first two dipole bands, containing some strong coupling cavity modes, propagate. This report also presents the dependence of the localized dipole modes on the transverse beam position. Linear dependence for various modes has been observed. This makes them suitable for beam position diagnostics. These modes, together with some propagating, strong coupling modes, have been considered in the design of a dedicated electronics for beam diagnostics with HOMs for the third harmonic cavities.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

HOM Choice Study with Test Electronics for use as Beam Position Diagnostics in 3.9 GHz Accelerating Cavities in FLASH

Higher Order Modes (HOM) excited by the beam in the 3.9 GHz accelerating cavities in FLASH can be used for beam position diagnostics, as in a cavity beam position monitor. Previous studies of the modal choices within the complicated spectrum have revealed several options: cavity modes with strong coupling to the beam, and therefore with the potential for better position resolution, but which are propagating within all 4 cavities, and modes localized in the cavities or the beam pipes, which can give localized position information, but which provide worse resolution. For a better characterization of these options, a set of test electronics has been built, which can down-convert various frequencies between about 4 and 9 GHz to 70 MHz. The performance of various 20 MHz bands has been estimated. The best resolution of 20 μm was found for some propagating modes. Based on this study one band at ca. 5 GHz was chosen for high resolution position monitoring and a band at ca. 9 GHz for localized monitoring.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Identifying the orbital angular momentum of light based on atomic ensembles

We propose a scheme to distinguish the orbital angular momentum state of the Laguerre-Gaussian (LG) beam based on the electromagnetically induced transparency modulated by a microwave field in atomic ensembles. We show that the transverse phase variation of a probe beam with the LG mode can be mapped into the spatial intensity distribution due to the change of atomic coherence caused by the microwave. The proposal may provide a useful tool for studying higher-dimensional quantum information based on atomic ensembles.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Implementing the Deutsch's algorithm with spin-orbital angular momentum of photon without interferometer

Deutsch's algorithm is the simplest quantum algorithm which shows the acceleration of quantum computer. In this paper, we theoretically advance a scheme to implement quantum Deutsch's algorithm in spin-orbital angular momentum space. Our scheme exploits a newly developed optical device called "q-plate", which can couple and manipulate the spin-orbital angular momentum simultaneously. This experimental setup is of high stability and efficiency theoretically for there is no interferometer in it.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Spin polarization separation of reflected light at Brewster angle

A novel spin polarization separation of reflected light is observed, when a linearly polarized Gaussian beam impinges on an air-glass interface at Brewster angle. In the far-field zone, spins of photons are oppositely polarized in two regions along the direction perpendicular to incident plane. Spatial scale of this polarization is related to optical properties of dielectric and can be controlled by experimental configuration. We believe that this study benefits the manipulation of spins of photons and the development of methods for investigating optical properties of materials.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Variation of polarization distribution of reflected beam caused by spin separation

The variation of polarization distribution of reflected beam at specular interface and far field caused by spin separation has been studied. Due to the diffraction effect, we find a distinct difference of light polarization at the two regions. The variation of polarization distribution of reflected light provides a new method to measure the spin separation displacement caused by Spin Hall Effect of light.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Entanglement distillation from Gaussian input states by coherent photon addition

The entanglement between Gaussian entangled states can be increased by non-Gaussian operations. We design a new scheme named coherent photon addition, which can coherently add one photon generated by spontaneous parametric down conversation process to Gaussian quadrature-entangled light pulses created by a non-degenerate optical parametric amplifier. This operation can increase the entanglement of input two-mode Gaussian states as an entanglement distillation, and provides us a new method of non-Gaussian operation. This scheme can also help us to study the decoherence of adding one to two-mode Gaussian states from coherent photon addition to normal photon addition.

0 citations0 reviewsNo code linkedOpen access
preprint2010arXiv

Implementation of one-dimensional quantum walks on spin-orbital angular momentum space of photons

Photons can carry spin angular momentum (SAM) and orbital angular momentum (OAM), which can be used to realize a qubit system and a high-dimension system respectively. This spin-orbital system is very suitable for implementing one-dimensional discrete-time quantum random walks. We propose a simple scheme of quantum walks on the spin-orbital angular momentum space of photons, where photons walk on the infinity OAM space controlled by their SAM. By employing the recent invention of an optical device, the so-called 'q-plate', our scheme is more simple and efficient than others because there is no Mach-Zehnder interferometer in the scheme.

0 citations0 reviewsNo code linkedOpen access