Vector magnetometry in zero bias magnetic field using nitrogen-vacancy ensembles

The application of the vector magnetometry based on nitrogen-vacancy(NV)ensembles has been widely investigatedin multiple areas.It has the superiority of high sensitivity and high stability in ambient conditions with microscale spatialresolution.However,a bias magnetic field is necessary to fully separate the resonance lines of optically detected magneticresonance(ODMR)spectrum of NV ensembles.This brings disturbances in samples being detected and limits the rangeof application.Here,we demonstrate a method of vector magnetometry in zero bias magnetic field using NV ensembles.By utilizing the anisotropy property of fluorescence excited from NV centers,we analyzed the ODMR spectrum of NVensembles under various polarized angles of excitation laser in zero bias magnetic field with a quantitative numerical modeland reconstructed the magnetic field vector.The minimum magnetic field modulus that can be resolved accurately is downto~0.64 G theoretically depending on the ODMR spectral line width(1.8 MHz),and~2 G experimentally due to noisesin fluorescence signals and errors in calibration.By using 13C purified and low nitrogen concentration diamond combinedwith improving calibration of unknown parameters,the ODMR spectral line width can be further decreased below 0.5 MHz,corresponding to~0.18 G minimum resolvable magnetic field modulus.

Physics-Constrained Robustness Enhancement for Tree Ensembles Applied in Smart Grid

With the widespread use of machine learning(ML)technology,the operational efficiency and responsiveness of power grids have been significantly enhanced,allowing smart grids to achieve high levels of automation and intelligence.However,tree ensemble models commonly used in smart grids are vulnerable to adversarial attacks,making it urgent to enhance their robustness.To address this,we propose a robustness enhancement method that incorporates physical constraints into the node-splitting decisions of tree ensembles.Our algorithm improves robustness by developing a dataset of adversarial examples that comply with physical laws,ensuring training data accurately reflects possible attack scenarios while adhering to physical rules.In our experiments,the proposed method increased robustness against adversarial attacks by 100%when applied to real grid data under physical constraints.These results highlight the advantages of our method in maintaining efficient and secure operation of smart grids under adversarial conditions.

Engineering Vacancy-Atom Ensembles to Boost Catalytic Activity toward Hydrogen Evolution

The dissociation of water is the rate-determining step of several energy-relating reactions due to high energy barrier in homolysis of H-O bond.Herein,engineering vacancy-atom ensembles via injecting oxygen vacancy(V O)into single facet-exposed TiO_(2)-Pd catalyst to form V_(O)-Pd ensemble is proposed and implemented.The outstanding activity of as-prepared catalyst,1.5-PdTV_(O),toward water dissociation is established with a turnover frequency of 240 min^(−1) in ammonia borane hydrolysis at 298 K.Density functional theory simulation suggests that the V_(O)-Pd ensemble is responsible for the high intrinsic catalytic activity.Water molecules tend to be dissociated on V_(O) sites and ammonia borane molecules on Pd atoms.Those H atoms from water dissociation on V_(O) combine with H atoms from ammonia borane on Pd atoms to generate H_(2).This insights into engineering vacancy-atom ensembles catalysis provide a new avenue to design catalytic materials for important energy chemical reactions.

ENSEMBLES耦合模式对中国冬季气候预测能力的评估

利用ENSEMBLES (Ensemble-Based Predictions of Climate Changes and Their Impacts)计划中提供的5个全球海气耦合模式回报数据,评估了模式对1980~2005年中国地区(15˚~55˚N,70˚~140˚E)冬季(12月、1月和2月)降水,风场以及2 m气温气候态的预测能力。结果表明:模式能较好地预测出中国地区冬季降水主要集中在东南地区的特征,但高估了东南地区的降水,且青藏高原东部边缘存在虚假的降水中心;模式低估了青藏高原北部的偏西风,高估了中国大部地区的2 m气温,新疆地区和西南地区出现了冷偏差。对ENSEMBLES预测的冬季降水和2 m气温进行经验正交函数(Empirical Orthogonal Function, EOF)分析,发现模式可以预测出中国冬季降水和2 m气温变化的主要空间模态,各个模式的预测效果在不同的程度上受起报时间的影响。

LARGE DEVIATIONS FOR TOP EIGENVALUES OFβ-JACOBI ENSEMBLES AT SCALING TEMPERATURES

Letλ=(λ_(1),…,λ_(n))beβ-Jacobi ensembles with parameters p_(1),p_(2),n andβ,withβvarying with n.Set■.Suppose that■and 0≤σγ<1.We offer the large deviation for p_(1)+p_(2)/p_(1)■λ_(i)whenγ>0 via the large deviation of the corresponding empirical measure and via a direct estimate,respectively,whenγ=0.

A Survey on Acute Leukemia Expression Data Classification Using Ensembles

Acute leukemia is an aggressive disease that has high mortality rates worldwide.The error rate can be as high as 40%when classifying acute leukemia into its subtypes.So,there is an urgent need to support hematologists during the classification process.More than two decades ago,researchers used microarray gene expression data to classify cancer and adopted acute leukemia as a test case.The high classification accuracy they achieved confirmed that it is possible to classify cancer subtypes using microarray gene expression data.Ensemble machine learning is an effective method that combines individual classifiers to classify new samples.Ensemble classifiers are recognized as powerful algorithms with numerous advantages over traditional classifiers.Over the past few decades,researchers have focused a great deal of attention on ensemble classifiers in a wide variety of fields,including but not limited to disease diagnosis,finance,bioinformatics,healthcare,manufacturing,and geography.This paper reviews the recent ensemble classifier approaches utilized for acute leukemia gene expression data classification.Moreover,a framework for classifying acute leukemia gene expression data is proposed.The pairwise correlation gene selection method and the Rotation Forest of Bayesian Networks are both used in this framework.Experimental outcomes show that the classification accuracy achieved by the acute leukemia ensemble classifiers constructed according to the suggested framework is good compared to the classification accuracy achieved in other studies.

Identification Method for Users-Transformer Relationship in Station Area Based on Local Selective Combination in Parallel Outlier Ensembles Algorithm

In the power distribution system,the missing or incorrect file of users-transformer relationship(UTR)in lowvoltage station area(LVSA)will affect the leanmanagement of the LVSA,and the operation andmaintenance of the distribution network.To effectively improve the lean management of LVSA,the paper proposes an identification method for the UTR based on Local Selective Combination in ParallelOutlier Ensembles algorithm(LSCP).Firstly,the voltage data is reconstructed based on the information entropy to highlight the differences in between.Then,the LSCP algorithmcombines four base outlier detection algorithms,namely Isolation Forest(I-Forest),One-Class Support VectorMachine(OC-SVM),Copula-Based Outlier Detection(COPOD)and Local Outlier Factor(LOF),to construct the identification model of UTR.This model can accurately detect users’differences in voltage data,and identify users with wrong UTR.Meanwhile,the key input parameter of the LSCP algorithm is determined automatically through the line loss rate,and the influence of artificial settings on recognition accuracy can be reduced.Finally,thismethod is verified in the actual LVSA where the recall and precision rates are 100%compared with othermethods.Furthermore,the applicability to the LVSAs with difficult data acquisition and the voltage data error in transmission are analyzed.The proposed method adopts the ensemble learning framework and does not need to set the detection threshold manually.And it is applicable to the LVSAs with difficult data acquisition and high voltage similarity,which improves the stability and accuracy of UTR identification in LVSA.

Improving Land Resource Evaluation Using Fuzzy Neural Network Ensembles

Land evaluation factors often contain continuous-, discrete- and nominal-valued attributes. In traditional land evaluation, these different attributes are usually graded into categorical indexes by land resource experts, and the evaluation results rely heavily on experts＇ experiences. In order to overcome the shortcoming, we presented a fuzzy neural network ensemble method that did not require grading the evaluation factors into categorical indexes and could evaluate land resources by using the three kinds of attribute values directly. A fuzzy back propagation neural network （BPNN）, a fuzzy radial basis function neural network （RBFNN）, a fuzzy BPNN ensemble, and a fuzzy RBFNN ensemble were used to evaluate the land resources in Guangdong Province. The evaluation results by using the fuzzy BPNN ensemble and the fuzzy RBFNN ensemble were much better than those by using the single fuzzy BPNN and the single fuzzy RBFNN, and the error rate of the single fuzzy RBFNN or fuzzy RBFNN ensemble was lower than that of the single fuzzy BPNN or fuzzy BPNN ensemble, respectively. By using the fuzzy neural network ensembles, the validity of land resource evaluation was improved and reliance on land evaluators＇ experiences was considerably reduced.

Recent Progress on Two-Dimensional Nanoflake Ensembles for Energy Storage Applications

The rational design and synthesis of two-dimensional(2D) nanoflake ensemble-based materials have garnered great attention owing to the properties of the components of these materials, such as high mechanical flexibility, high specific surface area, numerous active sites,chemical stability, and superior electrical and thermal conductivity. These properties render the 2D ensembles great choices as alternative electrode materials for electrochemical energy storage systems. More recently,recognition of the numerous advantages of these 2D ensemble structures has led to the realization that the performance of certain devices could be significantly enhanced by utilizing three-dimensional(3D) architectures that can furnish an increased number of active sites. The present review summarizes the recent progress in 2D ensemble-based materials for energy storage applications,including supercapacitors, lithium-ion batteries, and sodium-ion batteries. Further, perspectives relating to the challenges and opportunities in this promising research area are discussed.

Predictability of Winter Rainfall in South China as Demonstrated by the Coupled Models of ENSEMBLES

Winter rainfall over South China shows strong interannual variability,which accounts for about half of the total winter rainfall over South China.This study investigated the predictability of winter （December-January-February; DJF） rainfall over South China using the retrospective forecasts of five state-of-the-art coupled models included in the ENSEMBLES project for the period 1961-2006.It was found that the ENSEMBLES models predicted the interannual variation of rainfall over South China well,with the correlation coefficient between the observed/station-averaged rainfall and predicted/areaaveraged rainfall being 0.46.In particular,above-normal South China rainfall was better predicted,and the correlation coefficient between the predicted and observed anomalies was 0.64 for these wetter winters.In addition,the models captured well the main features of SST and atmospheric circulation anomalies related to South China rainfall variation in the observation.It was further found that South China rainfall,when predicted according to predicted DJF Nifio3.4 index and the ENSO-South China rainfall relationship,shows a prediction skill almost as high as that directly predicted,indicating that ENSO is the source for the predictability of South China rainfall.

Predictability of the East Asian Winter Monsoon Indices by the Coupled Models of ENSEMBLES

The seasonal predictability of various East Asian winter monsoon （EAWM） indices was investigated in this study based on the retrospective forecasts of the five state-of-the-art coupled models from ENSEMBLES for a 46-year period of 19612006.It was found that the ENSEMBLES models predict five out of the 21 EAWM indices well,with temporal correlation coefficients ranging from 0.54 to 0.61.These five indices are defined by the averaged lower-tropospheric winds over the low latitudes （south of 30°N）.Further analyses indicated that the predictability of these five indices originates from their intimate relationship with ENSO.A cross-validated prediction,which took the preceding （November） observed Nifo3.4 index as a predictor,gives a prediction skill almost identical to that shown by the model.On the other hand,the models present rather low predictability for the other indices and for surface air temperature in East Asia.In addition,the models fail to reproduce the relationship between the indices of different categories,implying that they cannot capture the tropicalextratropical interaction related to EAWM variability.Together,these results suggest that reliable prediction of the EAWM indices and East Asian air temperature remains a challenge.

Preparation of Nanoelectrode Ensembles by Assembly of Nano-Silver Colloid on Gold Surface

A novel method for preparing silver nanoelectrode ensembles (SNEEs) and gold nanoelectrode ensembles (GNEEs) has been developed. Silver colloid particles were first absorbed to the gold electrode surface to form a monolayer silver colloid. N-hexadecyl mercaptan was then assembled on the electrode to form a thiol monolayer on which hydrophilic ions cannot be transfered. The SNEEs was prepared by removing thiol from silver colloid surface through applying an AC voltage with increasing frequency at 0.20 V (vs. SCE). Finally, GNEEs was obtained by immersing a SNEEs into 6 mol/L HNO3 to remove the silver colloid particles. By comparison with other methods such as template method etc., this method enjoys some advantages of lower resistance, same diameter, easy preparation, controllable size and density.

No-relationship Between Impossibility of Faster-Than-Light Quantum Communication and Distinction of Ensembles with the Same Density Matrix

It has been claimed in the literature that impossibility of faster-than-light quantum communication has an origin of indistinguishability of ensembles with the same density matrix. We show that the two concepts are not related.We argue that even with an ideal single-atom-precision measurement, it is generally impossible to produce two ensembles with exactly the same density matrix; or to produce ensembles with the same density matrix, classical communication is necessary. Hence the impossibility of faster-than-light communication does not imply the indistinguishability of ensembles with the same density matrix.

Secure Direct Communication Using Ensembles with the Same Compressed Density Matrix

In this paper, a quantum secure direct communication protocol using ensembles with the same density matrix is proposed. The two communication parties can realize the message transmission using this method through a quantum channel, each bit of information can be transmitted using an ensemble and read out through global measurement. The eavesdropping behavior can be detected through the channel diagnoses.

Scheme for preparation of multi-partite entanglement of atomic ensembles

We show a scheme of preparing multipartite W type of maximally entangled states among many atomic ensembles with the generation time increasing with the party number only polynomially. The scheme is based on laser manipulation of atomic ensembles and single-photon detection, and fits well the status of the current experimental technology. We also show one of the applications of this kind of W state, demonstrating Bell theorem without inequalities.

Seasonal Prediction Assessment of the South Asian Summer Monsoon: ENSEMBLES versus DEMETER

The seasonal forecasting skill with respect to the South Asian summer monsoon(SASM) was compared between the European Commission FP7 project(ENSEMBLES) and the Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction project(DEMETER). The Webster-Yang index(WYI) was chosen to represent the intensity of the SASM. First, the authors compared the ability to forecast the zonal wind at 850 h Pa(U850) and 200 h Pa(U200) between ENSEMBLES and DEMETER models. The results indicated that the models from the European Centre for Medium-Range Weather Forecasts, International Organization(ECMWF) and UK Met Office(UKMO) in ENSEMBLES possess greater skill in seasonally forecasting the JJA(June, July, and August) U850, U200, and U850 minus U200 than in DEMETER. Compared to in DEMETER, the JJA U200 and U850 minus U200 forecasting skill was greater for the model from MétéoFrance(MF) in ENSEMBLES over most of the SASM region. The three coupled models(ECMWF, MF, and UKMO), especially the UKMO model in ENSEMBLES, all demonstrated improved skill in their seasonal forecasts compared to in DEMETER with respect to the interannual variability of the SASM. The three ENSEMBLES models also showed better ability in forecasting the sea surface temperature anomalies(SSTAs) over the eastern equatorial Pacific and North Indian Ocean, and more accurately reproduced the large-scale atmospheric circulation and precipitation over northern India, which are related to the SASM. It seems that the couple between the atmospheric system and external forcing of ENSMBLES over Indian Ocean and Pacific is better than that of DEMETER.

Ensemble Hindcasts of ENSO Events over the Past 120 Years Using a Large Number of Ensembles

Based on an intermediate coupled model （ICM）, a probabilistic ensemble prediction system （EPS） has been developed. The ensemble Kalman filter （EnKF） data assimilation approach is used for generating the initial ensemble conditions, and a linear, first-order Markov-Chain SST anomaly error model is embedded into the EPS to provide model-error perturbations. In this study, we perform ENSO retrospective forecasts over the 120 year period 1886-2005 using the EPS with 100 ensemble members and with initial conditions obtained by only assimilating historic SST anomaly observations. By examining the retrospective ensemble forecasts and available observations, the verification results show that the skill of the ensemble mean of the EPS is greater than that of a single deterministic forecast using the same ICM, with a distinct improvement of both the correlation and root mean square （RMS） error between the ensemble-mean hindcast and the deterministic scheme over the 12-month prediction period. The RMS error of the ensemble mean is almost 0.2℃ smaller than that of the deterministic forecast at a lead time of 12 months. The probabilistic skill of the EPS is also high with the predicted ensemble following the SST observations well, and the areas under the relative operating characteristic （ROC） curves for three different ENSO states （warm events, cold events, and neutral events） are all above 0.55 out to 12 months lead time. However, both deterministic and probabilistic prediction skills of the EPS show an interdecadal variation. For the deterministic skill, there is high skill in the late 19th century and in the middle-late 20th century （which includes some artificial skill due to the model training period）, and low skill during the period from 1906 to 1961. For probabilistic skill, for the three different ENSO states, there is still a similar interdecadal variation of ENSO probabilistic predictability during the period 1886~2005. There is high skill in the late 19th century from 1886 to 1905, and a decline to a minimum of skill around 1910-50s, beyond which skill rebounds and increases with time until the 2000s.

Numerical simulation of an extreme haze pollution event over the North China Plain based on initial and boundary condition ensembles

The North China Plain often su ers heavy haze pollution events in the cold season due to the rapid industrial development and urbanization in recent decades.In the winter of 2015,the megacity cluster of Beijing Tianjin Hebei experienced a seven-day extreme haze pollution episode with peak PM2.5(particulate matter(PM)with an aerodynamic diameter≤2.5μm)concentration of 727μg m 3.Considering the in uence of meteorological conditions on pollu-tant evolution,the e ects of varying initial conditions and lateral boundary conditions(LBCs)of the WRF-Chem model on PM2.5 concentration variation were investigated through ensemble methods.A control run(CTRL)and three groups of ensemble experiments(INDE,BDDE,INBDDE)were carried out based on difierent initial conditions and LBCs derived from ERA5 reanalysis data and its 10 ensemble members.The CTRL run reproduced the meteorological conditions and the overall life cycle of the haze event reasonably well,but failed to capture the intense oscillation of the instantaneous PM2.5 concentration.However,the ensemble forecasting showed a considerable advantage to some extent.Compared with the CTRL run,the root-mean-square error(RMSE)of PM2.5 concentration decreased by 4.33%,6.91%,and 8.44%in INDE,BDDE and INBDDE,respectively,and the RMSE decreases of wind direction(5.19%,8.89%and 9.61%)were the dominant reason for the improvement of PM2.5 concentration in the three ensemble experiments.Based on this case,the ensemble scheme seems an e ective method to improve the prediction skill of wind direction and PM2.5 concentration by using the WRF-Chem model.

Quantum state transfer between atomic ensembles trapped in separate cavities via adiabatic passage

We propose a new approach for quantum state transfer（QST） between atomic ensembles separately trapped in two distant cavities connected by an optical fiber via adiabatic passage. The three-level Λ-type atoms in each ensemble dispersively interact with the nonresonant classical field and cavity mode. By choosing appropriate parameters of the system, the effective Hamiltonian describes two atomic ensembles interacting with ＂the same cavity mode＂ and has a dark state. Consequently, the QST between atomic ensembles can be implemented via adiabatic passage. Numerical calculations show that the scheme is robust against moderate fluctuations of the experimental parameters. In addition, the effect of decoherence can be suppressed effectively. The idea provides a scalable way to an atomic-ensemble-based quantum network, which may be reachable with currently available technology.

A Comparative Study of Machine Learning Algorithms and Their Ensembles for Botnet Detection

A Botnet is a network of compromised devices that are controlled by malicious “botmaster” in order to perform various tasks, such as executing DoS attack, sending SPAM and obtaining personal data etc. As botmasters generate network traffic while communicating with their bots, analyzing network traffic to detect Botnet traffic can be a promising feature of Intrusion Detection System. Although such system has been applying various machine learning techniques, comparison of machine algorithms including their ensembles on botnet detection has not been figured out. In this study, not only the three most popular classification machine learning algorithms—Naive Bayes, Decision tree, and Neural network are evaluated, but also the ensemble methods known to strengthen classifier are tested to see if they indeed provide enhanced predictions on Botnet detection. This evaluation is conducted with the CTU-13 public dataset, measuring the training time of each classifier and its F measure and MCC score.