Calculating the required cash in bank branches: a Bayesian-data mining approach

The issue of sufficiency of cash in bank branches is considered as an important issue especially for branch managers; because, not only the insufficiency of daily cash results in lack of response to needs of customers, but also may its excess result in increase in costs for banks. Hence, banks are always attempting to determine their required cash based on their daily operation. For this purpose, in this paper, 18 branches of a certain bank in a period of five months, due to diversity of the branches, have been classified by two methods of hierarchical clustering and Bayesian hierarchical clustering in similar clusters, and then by considering the results obtained from clustering, amounts of entered and consumed branch cash have been estimated by neural network (via classic and Bayesian approach), so that the cash required by branches can be calculated. The error criteria of the estimates show that calculations by applying Bayesian neural network method with considering Bayesian clustering have the least error compared to other methods.

     

The Precomputed-Branch architecture: Efficient branches with compiler support

Accurate instruction fetch and branch prediction is increasingly important in today's superscalar architectures. Fetch prediction is the process of determining the next instruction to request from the memory subsystem. Branch prediction is the process of predicting the likely outcome of branch instructions. A branch target buffer (BTB) is often used to provide target addresses for taken branches and to predict the destination of indirect jumps. Using a BTB avoids the delay needed to recalculate the destination address and reduces the misfetch penalty. However, an effective branch target buffer can be large and can possibly increase the cycle time of a processor. We propose that a design used in older computers, such as the PDP-8, be used in modern architectures instead of a BTB design. The compiler would pre-compute the branch destination for most branch instructions, allowing the branch information to be stored with the instruction. We consider computing branch destinations at link time and as instructions are fetched into the instruction cache; both alternatives offer similar performance with different advantages. A very small BTB is still useful to predict indirect branches, which cannot be pre-computed. Our results show that the Precomputed-Branch architecture performs better than an architecture using only a BTB, and has significant hardware savings. This is particularly true for larger programs more representative of modern applications.     

Inflation targeting as a signaling mechanism

This paper theoretically investigates inflation targeting when there is asymmetric information between the Central Bank and the public. The main argument of this study is that the inflation target can be used as a signaling mechanism through which the private sector learns about the private information of the Central Bank about future inflation and output. Thus, inflation targeting increases transparency and this causes the monetary policy actions (changes in the interest rate) to be more effective. I construct a Kalman filter algorithm to analyze the information and learning dynamics between the Central Bank and a representative private-sector agent. An increase (decrease) in the interest rate and the inflation target signals that the Central Bank has private information that inflation and output will be higher (lower) in the future thus the public expect inflation to be higher (lower) in the future. The main results of the paper are as follows. First, the private-sector agents (public) revise their expectations about future inflation and output after observing the actions of the Central Bank: changes in the interest rate and the inflation target. Second, in the case of inflation targeting, the response of inflation to a monetary policy shock (change in the interest rate) is higher than it is in the case of no inflation targeting. So, when there is inflation targeting the interest rate tool of the CB is more effective in decreasing inflation.     

Correction to `Allocating programs containing branches and loopswithin a multiple processor system'

A number of typographical errors in the above-mentioned paper (see ibid., vol.12, no.10, p.1018-24 (1986)) are corrected     

一种树木折枝现象的仿真算法

为了快速真实地模拟风力过大引起的树木折枝现象,提出了一种仿真算法。首先结合噪声函数给出一个风场模型,然后应用材料力学知识分析树枝的运动细节后得出树枝的变形参数,并将变形参数加入到树木的分形几何解释中,最后在此基础上设计了树木折枝的可视化仿真方法。该算法可以通过调整风场模型中的参数得到不同风力作用下树木折枝的可视化仿真效果。仿真结果表明了算法的正确性和有效性,能够为统计树木受灾情况提供参考依据。     

New branches of electrically charged Einstein-Yang-Mills-Higgs solutions

We consider dyons and electrically charged monopole-antimonopole pairs in Einstein-Yang-Mills-Higgs theory. In the presence of an electric charge, bifurcations arise and new branches of solutions appear. For large values of the electric charge, spherically symmetric dyons approach limiting solutions related to the Penney solution of Einstein-Maxwell-scalar theory.     

Automatic customer targeting: a data mining solution to the problem of asymmetric profitability distribution

Information Technology and Management - This paper proposes a data mining approach for automatic customer targeting based on their expected profitability. The main challenge with customer...     

COG: local decomposition for rare class analysis

Given its importance, the problem of predicting rare classes in large-scale multi-labeled data sets has attracted great attention in the literature. However, rare class analysis remains a critical challenge, because there is no natural way developed for handling imbalanced class distributions. This paper thus fills this crucial void by developing a method for classification using local clustering (COG). Specifically, for a data set with an imbalanced class distribution, we perform clustering within each large class and produce sub-classes with relatively balanced sizes. Then, we apply traditional supervised learning algorithms, such as support vector machines (SVMs), for classification. Along this line, we explore key properties of local clustering for a better understanding of the effect of COG on rare class analysis. Also, we provide a systematic analysis of time and space complexity of the COG method. Indeed, the experimental results on various real-world data sets show that COG produces significantly higher prediction accuracies on rare classes than state-of-the-art methods and the COG scheme can greatly improve the computational performance of SVMs. Furthermore, we show that COG can also improve the performances of traditional supervised learning algorithms on data sets with balanced class distributions. Finally, as two case studies, we have applied COG for two real-world applications: credit card fraud detection and network intrusion detection.     

AGENT TCL: targeting the needs of mobile computers

Mobile computers have become increasingly popular as users discover the benefits of having their electronic work available at all times. Using Internet resources from a mobile platform, however, is a major challenge. Mobile computers do not have a permanent network connection and are often disconnected for long periods. When the computer is connected, the connection is often prone to sudden failure, such as when a physical obstruction blocks the signal from a cellular modem. In addition, the network connection often performs poorly and can vary dramatically from one session to the next, since the computer might use different transmission channels at different locations. Finally, depending on the transmission channel, the computer might be assigned a different network address each time it reconnects. Mobile agents are one way to handle these unforgiving network conditions. A mobile agent is an autonomous program that can move from machine to machine in a heterogeneous network under its own control. It can suspend its execution at any point, transport itself to a new machine, and resume execution on the new machine from the point at which it left off. Agent Tcl is a mobile agent system whose agents can be written in Tcl, Java, and Scheme. Agent Tcl has extensive navigation and communication services, security mechanisms, and debugging and tracking tools. We focus on Agent Tcl's architecture and security mechanisms, its RPC system, and its docking system, which lets an agent move transparently among mobile computers, regardless of when they are connected to the network     

Analytic classification of plane branches up to multiplicity 4

We perform the analytic classification of plane branches of multiplicity less than or equal to four. This is achieved by computing a Standard Basis for the modules of Kähler differentials of such branches by means of the algorithm developed in [Hefez, A., Hernandes, M.E., 2007a. Standard bases for local rings of branches and their module of differentials. J. Symbolic Comput. 42, 178–191] and then applying the classification method for plane branches given in [Hefez, A., Hernandes, M.E., 2007b. The analytic classification of plane branches. arXiv:0707.4502].     

Measuring the true managerial efficiency of bank branches in Taiwan: A three-stage DEA analysis

This paper aims to explore the true managerial efficiencies of the branches of a case bank in Taiwan. With 123 branches of the case bank comprising the sample, the study finds that, after the adjustment of environmental factors and statistical noise, managerial efficiency values from a three-stage data envelopment analysis (DEA) varies significantly from the traditional DEA model. This finding suggests that environmental variables have significant effect on branch efficiency. Moreover, scale inefficiency is the major cause of operating inefficiency in the case bank, and most branches are operating at the stage of increasing return to scale. With regards the branches’ business scope, those that operate loan and wealth management services have better managerial efficiency than those that focus on wealth management only. In terms of deposit amount, branches with a higher deposit amount generate better managerial efficiency. Finally, the results for regional location show no significant effect on branches’ managerial efficiency in Taiwan.     

Guaranteed task deadlines for fault-tolerant workloads with conditional branches

This work examines scheduling for a real-time multiprocessor (MAFT) in which both hard deadlines and fault-tolerance are necessary system components. A workload for this system consists of a set of concurrent dependent tasks, each with some execution frequency; tasks are also fully ordered by priority. Fault tolerance mechanisms include hardware-supported voting on computation results as well as on task starts, task completions, and branch conditions. The distributed agreement mechanism used on system-level decisions adds a variable threading delay to the run time of each copy of a task. These delays make current schedule verification techniques inapplicable. In the most general execution profile, each processor in the system runs a subset of the tasks, with different tasks possibly having different frequencies. In this work, however, we restrict attention to a special class of workloads, termed uni-schedule, in which each processor executes the entire task set, using the multiple processors to implement full redundancy. In addition, all tasks are assumed to have the same periodicity. Given these restrictions, we produce stable schedules consistent with the initial workload specifications. Algorithms are first given for uni-schedule workloads with no run-time branches, and then for uni-schedule workloads with branches.     

Comments on `Allocating programs containing branches and loopswithin a multiple processor system' by D. Towsley

The assignment algorithms proposed by D. Towsley (see ibid., vol.12, no.10, p.1018-24 (1986)) may not minimize the total communication and execution costs as stated. An example where the proposed restricted assignment algorithm fails to find an optimal assignment is given, and modifications that allow the algorithm to properly consider execution costs are proposed. Additional changes needed to model communication costs correctly in many assignment problems are discussed     

Retrieval of chlorophyll for assimilating branches of a typical desert plant through inversed radiative transfer models

Estimation of vegetation chlorophyll content is crucial for understanding carbon balance and for assessing stress and vulnerability of desert ecosystems. This study evaluated LIBERTY and PROSPECT, both the radiative transfer models at leaf scale, for estimating the chlorophyll content of Haloxylon ammodendron assimilating branches inversely from measured reflectance spectra. The results showed that both original LIBERTY and PROSPECT exhibited tangible challenges for inversion using measured data. However, their calibrated versions were capable of accurate retrieval of chlorophyll content inversely from reflectance spectra. For calibrated LIBERTY, the inversed estimation recorded an R ² of 0.55 with an RMSE of 34.33 mg m^?2 over the entire measured chlorophyll range from 47.03 to 291.83 mg m^?2. For comparison, the R ² reached 0.53 with an RMSE of 34.76 mg m^?2 for the calibrated PROSPECT. Further validations with other independent data sets produced similar high chlorophyll estimation accuracies. Our results indicated that both LIBERTY and PROSPECT are applicable for estimating chlorophyll content inversely for assimilating branches of typical desert plants after careful calibration, which is a necessary prior when coupling with canopy models to make further stand level chlorophyll estimations.     

Determination of sample size in a rare event simulation method

A technique is proposed for evaluating the probability of aircraft collision by the importance sampling technique. Upper limits for the size of an auxiliary sample are found, which ensure the prescribed relative accuracy of probability evaluation. Theoretical conclusions are confirmed by numerical experiments. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 1, pp. 76–86, January–February 2006.     

Soft-spot analysis: targeting compound noise effects in nanometer circuits

Soft-spot analysis identifies regions in a circuit that are most susceptible to multiple noise sources and their compound effects so that designers can harden those spots for greater robustness. HSpice simulation validates the methodology's quality, and demonstration on a commercial embedded processor shows its scalability.