arXiv subjects

Lu Zhang

Publications and source records attributed to Lu Zhang.

At least 451 records · Page 25Linked to original sources

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↗

arXiv2016

Achieving non-discrimination in data release

Discrimination discovery and prevention/removal are increasingly important tasks in data mining. Discrimination discovery aims to unveil discriminatory practices on the protected attribute (e.g., gender) by analyzing the dataset of historical decision records, and discrimination prevention aims to remove discrimination by modifying the biased data before conducting predictive analysis. In this paper, we show that the key to discrimination discovery and prevention is to find the meaningful partitions that can be used to provide quantitative evidences for the judgment of discrimination. With the support of the causal graph, we present a graphical condition for identifying a meaningful partition. Based on that, we develop a simple criterion for the claim of non-discrimination, and propose discrimination removal algorithms which accurately remove discrimination while retaining good data utility. Experiments using real datasets show the effectiveness of our approaches.

cs.LG↗

arXiv2016

A causal framework for discovering and removing direct and indirect discrimination

Anti-discrimination is an increasingly important task in data science. In this paper, we investigate the problem of discovering both direct and indirect discrimination from the historical data, and removing the discriminatory effects before the data is used for predictive analysis (e.g., building classifiers). We make use of the causal network to capture the causal structure of the data. Then we model direct and indirect discrimination as the path-specific effects, which explicitly distinguish the two types of discrimination as the causal effects transmitted along different paths in the network. Based on that, we propose an effective algorithm for discovering direct and indirect discrimination, as well as an algorithm for precisely removing both types of discrimination while retaining good data utility. Different from previous works, our approaches can ensure that the predictive models built from the modified data will not incur discrimination in decision making. Experiments using real datasets show the effectiveness of our approaches.

cs.LG↗

arXiv2016

How Transferable are Neural Networks in NLP Applications?

Transfer learning is aimed to make use of valuable knowledge in a source domain to help model performance in a target domain. It is particularly important to neural networks, which are very likely to be overfitting. In some fields like image processing, many studies have shown the effectiveness of neural network-based transfer learning. For neural NLP, however, existing studies have only casually applied transfer learning, and conclusions are inconsistent. In this paper, we conduct systematic case studies and provide an illuminating picture on the transferability of neural networks in NLP.

cs.CL↗

arXiv2016

Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation

Using neural networks to generate replies in human-computer dialogue systems is attracting increasing attention over the past few years. However, the performance is not satisfactory: the neural network tends to generate safe, universally relevant replies which carry little meaning. In this paper, we propose a content-introducing approach to neural network-based generative dialogue systems. We first use pointwise mutual information (PMI) to predict a noun as a keyword, reflecting the main gist of the reply. We then propose seq2BF, a "sequence to backward and forward sequences" model, which generates a reply containing the given keyword. Experimental results show that our approach significantly outperforms traditional sequence-to-sequence models in terms of human evaluation and the entropy measure, and that the predicted keyword can appear at an appropriate position in the reply.

cs.CL↗

arXiv2016

A New Method for Computing $φ$-functions and Their Condition Numbers of Large Sparse Matrices

We propose a new method for computing the $φ$-functions of large sparse matrices with low rank or fast decaying singular values. The key is to reduce the computation of $φ_{\ell}$-functions of a large matrix to $φ_{\ell+1}$-functions of some $r$-by-$r$ matrices, where $r$ is the numerical rank of the large matrix in question. Some error analysis on the new method is given. Furthermore, we propose two novel strategies for estimating 2-norm condition numbers of the $φ$-functions. Numerical experiments illustrate the numerical behavior of the new algorithms and show the effectiveness of our theoretical results.

math.NA↗

arXiv2016

Distilling Word Embeddings: An Encoding Approach

Distilling knowledge from a well-trained cumbersome network to a small one has recently become a new research topic, as lightweight neural networks with high performance are particularly in need in various resource-restricted systems. This paper addresses the problem of distilling word embeddings for NLP tasks. We propose an encoding approach to distill task-specific knowledge from a set of high-dimensional embeddings, which can reduce model complexity by a large margin as well as retain high accuracy, showing a good compromise between efficiency and performance. Experiments in two tasks reveal the phenomenon that distilling knowledge from cumbersome embeddings is better than directly training neural networks with small embeddings.

cs.CL↗

arXiv2016

Application of Origen2.1 in the decay photon spectrum calculation of spallation products

Origen2.1 is a widely used computer code for calculating the burnup, decay, and processing of radioactive materials. However, the nuclide library of Origen2.1 is used for existing reactors like pressurized water reactor, to calculate the photon spectrum released by the decay of spallation products, we have made specific libraries for the ADS tungsten spallation target, based on the results given by a Monte Carlo code: FLUKA. All the data used to make the Origen2.1 libraries is obtained from Nuclear structure & decay Data (NuDat2.6). The accumulated activity of spallation products and the contribution of nuclides to photon emission are given in this paper.

nucl-th↗

arXiv2016

Resonance of Gaussian electromagnetic field to the high frequency gravitational waves

We consider a Gaussian Beam (GB) resonant system for high frequency gravitational waves (HFGWs) detection. At present, we find the optimal signal strength in theory through setting the magnetic component of GB in a standard gaussian form. Under the synchro-resonance condition, we study the signal strength (i.e., transverse perturbative photon fluxes) from the relic HFGWs (predicted by ordinary inflationary model) and the braneworld HFGWs (from braneworld scenarios). Both of them would generate potentially detectable transverse perturbative photon fluxes (PPFs). Furthermore we find optimal system parameters and the relationship between frequency and effective width of energy fluxes accumulation.

gr-qc↗