Zero-Mode Contribution in Nucleon-Delta Transition

We investigate the transition form factors between nucleon and $\Delta$(1232) particles by using a covariant quark-spectator-diquark field theory model in (3+1) dimensions. Performing a light-front calculation in parallel with the manifestly covariant calculation in light-front helicity basis, we examine the light-front zero-mode contribution to the helicity components of light-front good ("+") current matrix elements. Choosing the light-front gauge ($\epsilon^+_{h=\pm}=0$) with circular polarization in Drell-Yan-West frame, we find that only the helicity components $({1\over 2}, {1\over 2})$ and $({1\over 2},-{1\over 2})$ of the good current receive the zero-mode contribution. Taking into account the zero-mode, we find the prescription independence in obtaining the light-front solution of form factors from any three helicity matrix elements with smeared light-front wavefunctions. The angular condition, which guarantees the full covariance of different schemes, is recovered.Comment: 16 latex pages, 7 figures, to appear in PR

How Does the Low-Rank Matrix Decomposition Help Internal and External Learnings for Super-Resolution

Wisely utilizing the internal and external learning methods is a new challenge in super-resolution problem. To address this issue, we analyze the attributes of two methodologies and find two observations of their recovered details: 1) they are complementary in both feature space and image plane, 2) they distribute sparsely in the spatial space. These inspire us to propose a low-rank solution which effectively integrates two learning methods and then achieves a superior result. To fit this solution, the internal learning method and the external learning method are tailored to produce multiple preliminary results. Our theoretical analysis and experiment prove that the proposed low-rank solution does not require massive inputs to guarantee the performance, and thereby simplifying the design of two learning methods for the solution. Intensive experiments show the proposed solution improves the single learning method in both qualitative and quantitative assessments. Surprisingly, it shows more superior capability on noisy images and outperforms state-of-the-art methods

Strong Optical and UV Intermediate-Width Emission Lines in the Quasar SDSS J232444.80-094600.3: Dust-Free and Intermediate-Density Gas at the Skin of Dusty Torus ?

Emission lines from the broad emission line region (BELR) and the narrow emission line region (NELR) of active galactic nuclei (AGNs) are extensively studied. However, between these two regions emission lines are rarely detected. We present a detailed analysis of a quasar SDSS J232444.80-094600.3 (SDSS J2324$-$0946), which is remarkable for its strong intermediate-width emission lines (IELs) with FWHM $\approx$ 1800 \kmps. The IEL component is presented in different emission lines, including the permitted lines \lya\ $\lambda$1216, \civ\ $\lambda$1549, semiforbidden line \ciii\ $\lambda$1909, and forbidden lines \oiii\ $\lambda\lambda$4959, 5007. With the aid of photo-ionization models, we found that the IELs are produced by gas with a hydrogen density of $n_{\rm H} \sim 10^{6.2}-10^{6.3}~\rm cm^{-3}$, a distance to the central ionizing source of $R \sim 35-50$ pc, a covering factor of CF $\sim$ 6\%, and a dust-to-gas ratio of $\leq 4\%$ times of SMC. We suggest that the strong IELs of this quasar are produced by nearly dust-free and intermediate-density gas located at the skin of the dusty torus. Such strong IELs, served as a useful diagnose, can provide an avenue to study the properties of gas between the BELR and the NELR

H∞ filtering for nonlinear discrete-time stochastic systems with randomly varying sensor delays

This is the post print version of the article. The official published version can be obained from the link - Copyright 2009 Elsevier LtdThis paper is concerned with the H∞ filtering problem for a general class of nonlinear discrete-time stochastic systems with randomly varying sensor delays, where the delayed sensor measurement is governed by a stochastic variable satisfying the Bernoulli random binary distribution law. In terms of the Hamilton–Jacobi–Isaacs inequalities, preliminary results are first obtained that ensure the addressed system to possess an l2-gain less than a given positive scalar γ. Next, a sufficient condition is established under which the filtering process is asymptotically stable in the mean square and the filtering error satisfies the H∞ performance constraint for all nonzero exogenous disturbances under the zero-initial condition. Such a sufficient condition is then decoupled into four inequalities for the purpose of easy implementation. Furthermore, it is shown that our main results can be readily specialized to the case of linear stochastic systems. Finally, a numerical simulation example is used to demonstrate the effectiveness of the results derived.This paper was not presented at any IFAC meeting. This paper was recommended for publication in revised form by Associate Editor James Lam under the direction of Editor Ian R. Petersen. This work was supported by the Shanghai Natural Science Foundation under Grant 07ZR14002, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Royal Society of the UK and the Alexander von Humboldt Foundation of Germany

Adversarial Attack and Defense on Graph Data: A Survey

Deep neural networks (DNNs) have been widely applied to various applications including image classification, text generation, audio recognition, and graph data analysis. However, recent studies have shown that DNNs are vulnerable to adversarial attacks. Though there are several works studying adversarial attack and defense strategies on domains such as images and natural language processing, it is still difficult to directly transfer the learned knowledge to graph structure data due to its representation challenges. Given the importance of graph analysis, an increasing number of works start to analyze the robustness of machine learning models on graph data. Nevertheless, current studies considering adversarial behaviors on graph data usually focus on specific types of attacks with certain assumptions. In addition, each work proposes its own mathematical formulation which makes the comparison among different methods difficult. Therefore, in this paper, we aim to survey existing adversarial learning strategies on graph data and first provide a unified formulation for adversarial learning on graph data which covers most adversarial learning studies on graph. Moreover, we also compare different attacks and defenses on graph data and discuss their corresponding contributions and limitations. In this work, we systemically organize the considered works based on the features of each topic. This survey not only serves as a reference for the research community, but also brings a clear image researchers outside this research domain. Besides, we also create an online resource and keep updating the relevant papers during the last two years. More details of the comparisons of various studies based on this survey are open-sourced at https://github.com/YingtongDou/graph-adversarial-learning-literature.Comment: In submission to Journal. For more open-source and up-to-date information, please check our Github repository: https://github.com/YingtongDou/graph-adversarial-learning-literatur

Single machine scheduling with exponential time-dependent learning effect and past-sequence-dependent setup times

AbstractIn this paper we consider the single machine scheduling problem with exponential time-dependent learning effect and past-sequence-dependent (p-s-d) setup times. By the exponential time-dependent learning effect, we mean that the processing time of a job is defined by an exponent function of the total normal processing time of the already processed jobs. The setup times are proportional to the length of the already processed jobs. We consider the following objective functions: the makespan, the total completion time, the sum of the quadratic job completion times, the total weighted completion time and the maximum lateness. We show that the makespan minimization problem, the total completion time minimization problem and the sum of the quadratic job completion times minimization problem can be solved by the smallest (normal) processing time first (SPT) rule, respectively. We also show that the total weighted completion time minimization problem and the maximum lateness minimization problem can be solved in polynomial time under certain conditions

Analytical Studies on a Modified Nagel-Schreckenberg Model with the Fukui-Ishibashi Acceleration Rule

We propose and study a one-dimensional traffic flow cellular automaton model of high-speed vehicles with the Fukui-Ishibashi-type (FI) acceleration rule for all cars, and the Nagel-Schreckenberg-type (NS) stochastic delay mechanism. By using the car-oriented mean field theory, we obtain analytically the fundamental diagrams of the average speed and vehicle flux depending on the vehicle density and stochastic delay probability. Our theoretical results, which may contribute to the exact analytical theory of the NS model, are in excellent agreement with numerical simulations.Comment: 3 pages previous; now 4 pages 2 eps figure

Model Development of a Blast Furnace Stove

AbstractA large amount of energy is required in the production of steel where the preheating of blast in the hot blast stoves for iron-making is one of the most energy-intensive processes. To improve the energy efficiency it is necessary to investigate how to improve the hot blast stove operation.In this work a mathematic model for evaluating the performance of the hot blast stove was developed using a finite difference approximation to represent the heat transfer inside the stove during operation. The developed model was calibrated by using the process data from the stove V26 at SSAB Oxelösund, Sweden. As a case study, the developed model was used to simulate the effect of a new concept of OxyFuel technique to hot blast stoves. The investigation shows that,by using the OxyFuel technique, it is possible to maintain the blast temperature while removing the usage of coke oven gas. Additionally, the hot blast temperature increases while the flue gas temperature decreases, which allows for an increase of the blast temperature, leading to improved energy efficiency for the hot stove system