On Weaknesses of Non–surjective Round Functions

We propose a new attack on Feistel ciphers with a non-surjective round function such as the CAST cipher family and LOKI91. We extend the attack towards block ciphers that use a non-uniformly distributed round function and apply the extended attack to the CAST family. This attack demonstrates that the round function of a Feistel cipher with six to eight rounds needs to be surjective and sufficiently uniform.     

激光等离子体物理实验数据格式研究

 鉴于现行激光等离子体物理实验数据属于无格式的原始数据，从数据分析的角度出发，在研究总结“星光”和“神光”实验数据的基础上，合理规划了激光等离子体物理实验数据文件格式，并据此重新编写了数据采集程序和数据格式转换程序。叙述了新格式数据文件的优点及应用。     

A bidirectional hybrid DWDM-PON employing optical injection locking technique and data comparators

A bidirectional hybrid dense-wavelength-division-multiplexing (DWDM)-passive optical network (PON) employing optical injection locking technique on vertical-cavity surface-emitting lasers (VCSELs) and data comparator, as well as optical injection locking technique on distributed feedback laser diodes (DFB LDs) and amplitude/phase compensator is proposed and demonstrated. Improved performances of bit error rate (BER) and carrier-to-noise ratio/composite second order/composite triple beat (CNR/CSO/CTB) were observed in our proposed bidirectional hybrid DWDM-PON. Bidirectional transmission in bidirectional hybrid DWDM-PON architecture is a very attractive option, the capacity of the optical network can be expanded relatively easily by bidirectional transmission technology.     

关于2×2矩阵代数保立方幂等的单射

刻画了2×2全矩阵代数保立方幂等映射,利用保立方幂等映射象与原象矩阵对应元素相等这个特点计算出相应的结果,得出特征不等于2,3,5域上2×2全矩阵代数保立方幂等单射的一个具体的刻画.     

Data-driven preventive maintenance for a heterogeneous machine portfolio

We describe a data-driven approach to optimize periodic maintenance policies for a heterogeneous portfolio with different machine profiles. When insufficient data are available per profile to assess failure intensities and costs accurately, we pool the data of all machine profiles and evaluate the effect of (observable) machine characteristics by calibrating appropriate statistical models. This reduces maintenance costs compared to a stratified approach that splits the data into subsets per profile and a uniform approach that treats all profiles the same.     

基于连续光谱法联合测定NH3-N、COD方法研究

针对地表水中Ⅰ—Ⅴ类水域、地下水、工业废水对氨氮及化学需氧量准确快速联合测定的需求,融合光谱分析法中的连续光谱法与顺序注射技术（SIA）,基于地表水国家检测标准,以氨态氮（NH₃-N）、化学需氧量（COD）原位水质参数为检测对象,设计了一种小型微量原位水质NH₃-N、COD高效快速检测仪。该系统主要依据自主设计的基于紫外灯照射消解与加热密闭消解方法同时进行的消解池结构以及基于光谱扫描设计的的检测池结构,达到快速消解稳定检测的目的。同时系统基于分光光度法优化了检测流程,测定开始COD消解的同时,待检测池中的NH₃-N指数显色后的配位化合物进行光谱扫描测定,消解后,进行COD的测定,整个检测过程相比国标检测法缩短至少60 min,可以实现25min内自动完成NH₃-N及COD的测定,大大节约时间成本。绘制经光谱扫描显色反应后配位化合物的吸光度与连续波长曲线可得：NH₃-N、COD分别于690和445 nm处具有明显吸收峰,经读取峰的吸光度值,采用最小二乘法分别建立NH₃-N、COD回归建模,拟合回归方程并计算相关系数,绘制相应参数的吸光度-浓度工作曲线。实验结果表明：在0~2 mg·L-1浓度范围内,NH₃-N标准工作曲线相关系数r≥0.998 7,且浓度与吸光度成正相关。重复性相对标准偏差为1.36%~1.68%,加标回收率为97%~102%;在0~50 mg·L-1浓度范围内,COD标准工作曲线相关系数r≥0.997 8,浓度与吸光度呈负相关。重复性相对标准偏差为2.14%~2.48%,加标回收率为97.6%~102.95%。系统的测定结果准确、直线性与稳定性良好,具有较高的可行性与可靠性。基于SIA与连续光谱法联合测定NH₃-N、COD的方法研究,为拓宽光谱法在水质快速检测领域的应用、提高检测效率等方面的研究具有重大价值。     

Gompertz zero-inflated cure rate regression models applied to credit risk data

In the financial market, it is important to consider that there is a proportion of customers that have settled their debt in time zero, immediately recovering their ability to pay. In this context, in this paper, we propose a survival analysis methodology that allows the insertion of times equal to zero in scenarios where credit risk is observed. The proposed model addresses the survival analysis model of the zero-inflated cure rate which incorporates the heterogeneity of three subgroups (individuals having events in the initial time, and individuals not susceptible and susceptible to the event). In our proposal, all available survival data of customers are modeled considering that the number of competitive causes follows a Poisson distribution and the baseline risk function follows a Gompertz distribution. The model parameter estimation is obtained by the maximum likelihood estimation procedure and simulation studies are conducted to evaluate the estimators' performance. The studied methodology will be applied to a credit database provided by a financial institution in Brazil.     

Quantum Bayesian computation

Quantum Bayesian computation is an emerging field that levers the computational gains available from quantum computers. They promise to provide an exponential speed-up in Bayesian computation. Our article adds to the literature in three ways. First, we describe how quantum von Neumann measurement provides quantum versions of popular machine learning algorithms such as Markov chain Monte Carlo and deep learning that are fundamental to Bayesian learning. Second, we describe quantum data encoding methods needed to implement quantum machine learning including the counterparts to traditional feature extraction and kernel embeddings methods. Third, we show how quantum algorithms naturally calculate Bayesian quantities of interest such as posterior distributions and marginal likelihoods. Our goal then is to show how quantum algorithms solve statistical machine learning problems. On the theoretical side, we provide quantum versions of high dimensional regression, Gaussian processes and stochastic gradient descent. On the empirical side, we apply a quantum FFT algorithm to Chicago house price data. Finally, we conclude with directions for future research.     

A Bayesian record linkage model incorporating relational data

In this article, we introduce a novel Bayesian approach for linking multiple social networks in order to discover the same real world person having different accounts across networks. In particular, we develop a latent model that allows us to jointly characterize the network and linkage structures relying on both relational and profile data. In contrast to other existing approaches in the machine learning literature, our Bayesian implementation naturally provides uncertainty quantification via posterior probabilities for the linkage structure itself or any function of it. Our findings clearly suggest that our methodology can produce accurate point estimates of the linkage structure even in the absence of profile information, and also, in an identity resolution setting, our results confirm that including relational data into the matching process improves the linkage accuracy. We illustrate our methodology using real data from popular social networks such as Twitter , Facebook , and YouTube .     

A review of Bayesian dynamic forecasting models: Applications in marketing

We briefly review the main developments of Bayesian dynamic models. The emphasis is on marketing applications. Typical examples in this area are discussed. The concepts of monitoring and intervention are carefully explained with illustrative examples and open source computational routines. We avoid algebraic developments and instead use graphical examples to illustrate theoretical aspects. Two real-world problems using Bayesian dynamic models are discussed. Finally, we describe recent developments and alternative proposals to formally address the dependence when dealing with the modeling of multiple time series.