arXiv subjects

Lu Zhang

Publications and source records attributed to Lu Zhang.

At least 451 records · Page 25Linked to original sources

arXiv2018

Scalable Generation of Multi-mode NOON States for Quantum Multiple-phase Estimation

Multi-mode NOON states have been attracting increasing attentions recently for their abilities of obtaining supersensitive and superresolved measurements for simultaneous multiple-phase estimation. In this paper, four different methods of generating multi-mode NOON states with high photon number are proposed. The first method is a linear optical approach that makes use of the Fock state filtration to reduce lower-order Fock state terms from the coherent state inputs, which are jointly combined to produce a multi-mode NOON state with the triggering of single-photon coincidence detections (SPCD) and appropriate postselection. The other three methods (two linear and one nonlinear) use N photon Fock states as the inputs and require SPCD triggering only. All of the four methods can theoretically create a multi-mode NOON state with arbitrary photon number. Comparisons among these four methods were made with respect to the feasibility and efficiency. The first method is experimentally most feasible since it takes considerably fewer photonic operations and, more importantly, requires neither the use of high-N Fock states nor high-degree of nonlinearity.

quant-ph↗

arXiv2018

Identifying Patch Correctness in Test-Based Program Repair

Test-based automatic program repair has attracted a lot of attention in recent years. However, the test suites in practice are often too weak to guarantee correctness and existing approaches often generate a large number of incorrect patches. To reduce the number of incorrect patches generated, we propose a novel approach that heuristically determines the correctness of the generated patches. The core idea is to exploit the behavior similarity of test case executions. The passing tests on original and patched programs are likely to behave similarly while the failing tests on original and patched programs are likely to behave differently. Also, if two tests exhibit similar runtime behavior, the two tests are likely to have the same test results. Based on these observations, we generate new test inputs to enhance the test suites and use their behavior similarity to determine patch correctness. Our approach is evaluated on a dataset consisting of 139 patches generated from existing program repair systems including jGenProg, Nopol, jKali, ACS and HDRepair. Our approach successfully prevented 56.3\% of the incorrect patches to be generated, without blocking any correct patches.

cs.SE↗

arXiv2018

Enhancing intrinsic detection efficiency of superconducting nanowire single-photon detectors via helium ion irradiation

Realizing an NbN superconducting nanowire single-photon detector (SNSPD) with a 100% intrinsic detection efficiency (IDE) at the near-infrared wavelengths is still challenging. Herein, we developed a post-processing method to increase the IDE of NbN SNSPDs to near unity using a 20 keV helium ion irradiation. The IDE enhancement was achieved owing to the ion-induced reduction of the superconducting energy gap and the electron density of states at the Fermi level, determined with the electrical and magnetic transport measurements. The change in optical absorptance of the irradiated SNSPD was negligible as confirmed by the measured optical reflectance and system detection efficiency (SDE). Benefited with the IDE enhancement, the SDE of an irradiated device was significantly increased from 49% to 92% at 2.2 K for a 1550 nm wavelength.

physics.app-ph↗

arXiv2018

FairGAN: Fairness-aware Generative Adversarial Networks

Fairness-aware learning is increasingly important in data mining. Discrimination prevention aims to prevent discrimination in the training data before it is used to conduct predictive analysis. In this paper, we focus on fair data generation that ensures the generated data is discrimination free. Inspired by generative adversarial networks (GAN), we present fairness-aware generative adversarial networks, called FairGAN, which are able to learn a generator producing fair data and also preserving good data utility. Compared with the naive fair data generation models, FairGAN further ensures the classifiers which are trained on generated data can achieve fair classification on real data. Experiments on a real dataset show the effectiveness of FairGAN.

cs.LG↗

arXiv2018

A New Approach for 4DVar Data Assimilation

Four-dimensional variational data assimilation (4DVar) has become an increasingly important tool in data science with wide applications in many engineering and scientific fields such as geoscience1-12, biology13 and the financial industry14. The 4DVar seeks a solution that minimizes the departure from the background field and the mismatch between the forecast trajectory and the observations within an assimilation window. The current state-of-the-art 4DVar offers only two choices by using different forms of the forecast model: the strong- and weak-constrained 4DVar approaches15-16. The former ignores the model error and only corrects the initial condition error at the expense of reduced accuracy; while the latter accounts for both the initial and model errors and corrects them separately, which increases computational costs and uncertainty. To overcome these limitations, here we develop an integral correcting 4DVar (i4DVar) approach by treating all errors as a whole and correcting them simultaneously and indiscriminately. To achieve that, a novel exponentially decaying function is proposed to characterize the error evolution and correct it at each time step in the i4DVar. As a result, the i4DVar greatly enhances the capability of the strong-constrained 4DVar for correcting the model error while also overcomes the limitation of the weak-constrained 4DVar for being prohibitively expensive with added uncertainty. Numerical experiments with the Lorenz model show that the i4DVar significantly outperforms the existing 4DVar approaches. It has the potential to be applied in many scientific and engineering fields and industrial sectors in the big data era because of its ease of implementation and superior performance.

physics.data-an↗

arXiv2018

Study of the abundance features of the metal-poor star HD 94028

Recent work has found that the metal-poor star HD 94028 shows interesting abundance features. The s-process material (e.g., Ba, La, Ce, and Pb) and r-process material (e.g., Eu, Os, Pt) are mildly overabundant while the element C is not enhanced. However, the observed supersolar ratio [As/Ge](= $0.99 \pm 0.23$) and subsolar ratio [Se/As](= $-0.16 \pm 0.22$) could not be fitted by the combination of s- and r-process abundances. In this work, adopting the abundance decomposition method, we investigate the astrophysical origins of the elements in this star. We find that the primary process of massive stars dominates the production of the light elements and iron-group elements. The lighter neutron-capture elements from Ge to Te mainly come from the weak r-process. The elements from Ba to Pb are synthesized dominantly by the main s- and main r-process. The observed abundance features of Ge, As, and Se in HD 94028 are mainly produced by the weak r-process, which possesses the features of the supersolar ratio [As/Ge] and subsolar ratio [Se/As]. Because Ge is not synthesized solely by the neutron-capture process, As should be the lightest neutron-capture element. Although the i-process has been suggested in several astrophysical environments, it should be superfluous to explaining the abundances of the lighter neutron-capture elements in HD 94028.

astro-ph.SR↗

arXiv2018

Formation of the Abundance Boundaries of the Heavier Neutron-capture Elements in Metal-poor Stars

The abundance scatter of heavier r-process elements (Z > 56) relative to Fe ([r/Fe]) in metal-poor stars preserves excellent information of the star formation history and provides important insights into the various situations of the Galactic chemical enrichment. In this respect, the upper and lower boundaries of [r/Fe] could present useful clues for investigating the extreme situations of the star formation history and the early Galactic chemical evolution. In this paper, we investigate the formation of the upper and lower boundaries of [r/Fe] for the gas clouds. We find that, for a cloud from which metal-poor stars formed, the formation of the upper limits of [r/Fe] is mainly due to the pollution from a single main r-process event. For a cloud from which metal-poor stars formed, the formation of the lower limits of [r/Fe] is mainly due to the pollution from a single SN II event that ejects primary Fe.

astro-ph.SR↗

arXiv2018

Achieving non-discrimination in prediction

Discrimination-aware classification is receiving an increasing attention in data science fields. The pre-process methods for constructing a discrimination-free classifier first remove discrimination from the training data, and then learn the classifier from the cleaned data. However, they lack a theoretical guarantee for the potential discrimination when the classifier is deployed for prediction. In this paper, we fill this gap by mathematically bounding the probability of the discrimination in prediction being within a given interval in terms of the training data and classifier. We adopt the causal model for modeling the data generation mechanism, and formally defining discrimination in population, in a dataset, and in prediction. We obtain two important theoretical results: (1) the discrimination in prediction can still exist even if the discrimination in the training data is completely removed; and (2) not all pre-process methods can ensure non-discrimination in prediction even though they can achieve non-discrimination in the modified training data. Based on the results, we develop a two-phase framework for constructing a discrimination-free classifier with a theoretical guarantee. The experiments demonstrate the theoretical results and show the effectiveness of our two-phase framework.

cs.LG↗

arXiv2018

On Discrimination Discovery and Removal in Ranked Data using Causal Graph

Predictive models learned from historical data are widely used to help companies and organizations make decisions. However, they may digitally unfairly treat unwanted groups, raising concerns about fairness and discrimination. In this paper, we study the fairness-aware ranking problem which aims to discover discrimination in ranked datasets and reconstruct the fair ranking. Existing methods in fairness-aware ranking are mainly based on statistical parity that cannot measure the true discriminatory effect since discrimination is causal. On the other hand, existing methods in causal-based anti-discrimination learning focus on classification problems and cannot be directly applied to handle the ranked data. To address these limitations, we propose to map the rank position to a continuous score variable that represents the qualification of the candidates. Then, we build a causal graph that consists of both the discrete profile attributes and the continuous score. The path-specific effect technique is extended to the mixed-variable causal graph to identify both direct and indirect discrimination. The relationship between the path-specific effects for the ranked data and those for the binary decision is theoretically analyzed. Finally, algorithms for discovering and removing discrimination from a ranked dataset are developed. Experiments using the real dataset show the effectiveness of our approaches.

cs.LG↗

arXiv2018

Crowd counting via scale-adaptive convolutional neural network

The task of crowd counting is to automatically estimate the pedestrian number in crowd images. To cope with the scale and perspective changes that commonly exist in crowd images, state-of-the-art approaches employ multi-column CNN architectures to regress density maps of crowd images. Multiple columns have different receptive fields corresponding to pedestrians (heads) of different scales. We instead propose a scale-adaptive CNN (SaCNN) architecture with a backbone of fixed small receptive fields. We extract feature maps from multiple layers and adapt them to have the same output size; we combine them to produce the final density map. The number of people is computed by integrating the density map. We also introduce a relative count loss along with the density map loss to improve the network generalization on crowd scenes with few pedestrians, where most representative approaches perform poorly on. We conduct extensive experiments on the ShanghaiTech, UCF_CC_50 and WorldExpo datasets as well as a new dataset SmartCity that we collect for crowd scenes with few people. The results demonstrate significant improvements of SaCNN over the state-of-the-art.

cs.CV↗

arXiv2017

Automated Refactoring of Nested-IF Formulae in Spreadsheets

Spreadsheets are the most popular end-user programming software, where formulae act like programs and also have smells. One well recognized common smell of spreadsheet formulae is nest-IF expressions, which have low readability and high cognitive cost for users, and are error-prone during reuse or maintenance. However, end users usually lack essential programming language knowledge and skills to tackle or even realize the problem. The previous research work has made very initial attempts in this aspect, while no effective and automated approach is currently available. This paper firstly proposes an AST-based automated approach to systematically refactoring nest-IF formulae. The general idea is two-fold. First, we detect and remove logic redundancy on the AST. Second, we identify higher-level semantics that have been fragmented and scattered, and reassemble the syntax using concise built-in functions. A comprehensive evaluation has been conducted against a real-world spreadsheet corpus, which is collected in a leading IT company for research purpose. The results with over 68,000 spreadsheets with 27 million nest-IF formulae reveal that our approach is able to relieve the smell of over 99\% of nest-IF formulae. Over 50% of the refactorings have reduced nesting levels of the nest-IFs by more than a half. In addition, a survey involving 49 participants indicates that for most cases the participants prefer the refactored formulae, and agree on that such automated refactoring approach is necessary and helpful.

cs.SE↗

arXiv2017

Gravitational lensing by a black hole with torsion

In this paper we have investigated the gravitational lensing in a spherically symmetric spacetime with torsion in the generalized Einstein-Cartan-Kibble-Sciama (ECKS) theory of gravity by considering higher order terms. The torsion parameters change the spacetime structure which affects the photon sphere, the deflection angle and the strong gravitational lensing. The condition of existence of horizons is not inconsistent with that of the photon sphere. Especially, there exists a novel case in which there is horizon but no photon sphere for the considered spacetime. In this special case, the deflection angle of the light ray near the event horizon also diverges logarithmically, but the coefficients in the strong-field limit are different from those in the cases with photon sphere. Moreover, in the far-field limit, we find that the deflection angle for certain torsion parameters approaches zero from the negative side, which is different from those in the usual spacetimes.

gr-qc↗

arXiv2017

Repulsive effect for the unbound high energy particles along the rotation axis in the Kerr-Taub-NUT spacetime

We have investigated the acceleration of the unbound high energy particles moving along the rotation axis in the Kerr-Taub-NUT spacetime, and then study the dependence of the repulsive effects on the NUT charge for the particles in the spacetime. Whether the repulsive effects with the NUT charge become stronger depends on the Carter constant, the position and velocity of the particles themselves. We also present numerically the changes of the observable velocity and acceleration with the NUT charge for the unbound particles in the Kerr-Taub-NUT spacetime.

gr-qc↗

arXiv2017

Anomalous excitation enhancement with Rydberg-dressed atoms

We develop the research achievement of recent work [M. Gärttner, et.al., Phys. Rev. Letts. 113, 233002 (2014)], in which an anomalous excitation enhancement is observed in a three-level Rydberg-atom ensemble with many-body coherence. In our novel theoretical analysis, this effect is ascribed to the existence of a quasi-dark state as well as its avoided crossings to nearby Rydberg-dressed states. Moreover, we show that with an appropriate control of the optical detuning to the intermediate state, the enhancement can evoke a direct facilitation to atom-light coupling that even breaks through the conventional $\sqrt{N}$ limit of strong-blockaded ensembles. As a consequence, the intensity of the probe laser for intermediate transition can be reduced considerably, increasing the feasibility of experiments with Rydberg-dressed atoms.

physics.atom-ph↗

arXiv2017

Time-periodic and stable patterns of a two-competing-species Keller-Segel chemotaxis model: effect of cellular growth

This paper investigates the formation of time--periodic and stable patterns of a two--competing--species Keller--Segel chemotaxis model with a focus on the effect of cellular growth. We carry out rigorous Hopf bifurcation analysis to obtain the bifurcation values, spatial profiles and time period associated with these oscillating patterns. Moreover, the stability of the periodic solutions is investigated and it provides a selection mechanism of stable time--periodic mode which suggests that only large domains support the formation of these periodic patterns. Another main result of this paper reveals that cellular growth is responsible for the emergence and stabilization of the oscillating patterns observed in the $3\times3$ system, while the system admits a Lyapunov functional in the absence of cellular growth. Global existence and boundedness of the system in 2D are proved thanks to this Lyapunov functional. Finally, we provide some numerical simulations to illustrate and support our theoretical findings.

math.AP↗

arXiv2017

An advanced three-axis elliptical hohlraum for indirectly driven inertial confinement fusion

The radiation symmetry and laser-plasma instabilities (LPIs) inside the conventional cylindrical hohlraum configuration are the two daunting challenges on the approach to ignition in indirectly driven inertial confinement fusion. Recently, near-vacuum cylindrical hohlraum (NVCH), octahedral spherical hohlraum (SH) and novel three-axis cylindrical hohlraum (TACH) were proposed to mitigate these issues. While the coupling efficiency might still be a critical risk. In this paper, an advanced three-axis elliptical hohlraum (TAEH) is proposed to make a compromise among these hohlraum performance. Preliminary simulations indicate that the TAEH (with a case-to-capsule ratio, CCR=2.8) could provide excellent radiation symmetry during the thorough laser pulse of the high-foot drive, comparable to the ones inside the SH (CCR=5.1) and TACH (CCR=2.2). The filling time of plasma affecting the LPIs is between those of SH and TACH, and about 1.5 times of that in the ignition hohlraum Rev5-CH of NIC and close to the one inside the NVCH (CCR=3.4). In particular, the coupling efficiency is about 22%, 29% and 17% higher than the one inside the NVCH, SH and TACH, respectively. It would be envisioned that the proposed hohlraum configuration merits consideration as an alternative route to indirect-drive ignition, complementary to the traditional cylindrical hohlraum and the proposed recently novel hohlraums.

physics.plasm-ph↗

arXiv2017

Quantum multi-parameter estimation with generalized balanced multi-mode NOON-like states

The simultaneous multi-parameter estimation problem using a class of multi-mode entangled states is investigated in this paper. Specifically, the problem of optical phase imaging is considered and the quantum probe is taken to be a balanced coherent superposition of components with an arbitrary quantum state in one mode and vacuum states in all the other modes, which is a generalization of the multi-mode NOON state. The analytical form for the quantum Cramer-Rao bound (QCRB) is presented, which shows the performance by providing a lower bound of the estimation uncertainty. It is shown that the NOON state has the worst performance among those in the class of the entangled states considered. We also analyze in detail four different scenarios, which are the NOON state, the entangled coherent state, the entangled squeezed coherent state, and the entangled squeezed vacuum state. From the comparison among these four states, we find that when the mean photon number is fixed, the squeezed vacuum state has the smallest QCRB, followed by the squeezed coherent state, entangled coherent state, and NOON state. We also illustrate that the balanced entangled state can perform better than a more generalized unbalanced form studied in previous works for certain scenarios. Finally, we give an experimental setup for producing a two-mode entangled state that can beat the NOON state in quantum metrology.

quant-ph↗

arXiv2017

Faster Mutation Analysis via Equivalence Modulo States

Mutation analysis has many applications, such as asserting the quality of test suites and localizing faults. One important bottleneck of mutation analysis is scalability. The latest work explores the possibility of reducing the redundant execution via split-stream execution. However, split-stream execution is only able to remove redundant execution before the first mutated statement. In this paper we try to also reduce some of the redundant execution after the execution of the first mutated statement. We observe that, although many mutated statements are not equivalent, the execution result of those mutated statements may still be equivalent to the result of the original statement. In other words, the statements are equivalent modulo the current state. In this paper we propose a fast mutation analysis approach, AccMut. AccMut automatically detects the equivalence modulo states among a statement and its mutations, then groups the statements into equivalence classes modulo states, and uses only one process to represent each class. In this way, we can significantly reduce the number of split processes. Our experiments show that our approach can further accelerate mutation analysis on top of split-stream execution with a speedup of 2.56x on average.

cs.SE↗