Efficient context-sensitive pointer analysis for C programs

Program analyses used in compilers commonly use a transfer function (TF) to summarize the input/output behavior of a procedure or region, speeding convergence of analysis of the surrounding region or program. A partial transfer function (PTF) summarizes input/output behavior for only a subset of the possible inputs. In many cases, an exact characterization of input/output behavior is possible for the contexts occurring in a given program, even when a concise and exact total summary would not be feasible. In other cases, an approximate PTF can be used which, while not exact, is more precise than would be possible with a total approximation.As demonstrated by prior work in a flow- and context-sensitive pointer alias analysis, PTFs are effective in speeding the convergence of program analysis involving higher-order functions and complex transfer functions that cannot easily be summarized.This paper presents a formal definition of partial transfer functions and introduces a general framework that enables data flow systems with complex transfer functions to be decomposed into simpler partial transfer functions. This paper provides a foundation that will allow the further development of the PTF concept, as well as enabling application to many more program analyses.

We present a demand-driven approach to memory leak detection algorithm based on flow- and context-sensitive pointer analysis. The detection algorithm firstly assumes the presence of a memory leak at some program point and then runs a backward analysis to see if this assumption can be disproved. Our algorithm computes the memory abstraction of programs based on points-to graph resulting from flow- and context-sensitive pointer analysis. We have implemented the algorithm in the SUIF2 compiler infrastructure and used the implementation to analyze a set of C benchmark programs. The experimental results show that the approach has better precision with satisfied scalability as expected.

The purpose of this study is to know the characteristic rice farming in The Village Kedung Banteng and analyze the economic efficiency and technical efficiency in rice farming in the Village Kedung Banteng.Total population of rice farmers in the village Kedung Banteng totaling 1825 people and taken 95 samples in . This research was conducted with qualitative and quantitative analysis based on production function with a stochastic frontier approach (Stochastic Frontier Production Function) included: regression analysis, maximum likelihood (MLE), the analysis of technical efficiency(DEA), economic efficiency and price efficiency.The results show the estimation of stochastic frontier production function rice farming both in The Village Kedung Banteng there are only four significant independent variables namely labor, land , seed and capital. When viewed from the coefficient of elasticity, these inputs is elastic and when viewed from its RTS, rice farming in a state of increasing returns to scale (IRS), which the IRS values of 1,8747 in The Village Kedung Banteng. Based on analysis of technical efficiency (ET), it is known there are only 21 farmers who are efficient in the Village Kedung Banteng at 100% efficiency rate. When viewed from the efficiency of the price / allocative and economic efficiency, the farming of rise has not been efficient with the efficiency of its value pricing 2,163 &1,758 in The Village Kedung Banteng economic efficiency . From the calculation of technical efficiency, price efficiency, and economic efficiency can be concluded that rice farming has not been efficient. Value R/C ratio is 1.34 means rice farming profitable.

Demand.- to Essays on Port Economics.- Port Marketing Strategies and the Challenges of Maritime Globalization.- Contextual Port Development: A Theoretical Approach.- The Conditioned Demands of General Merchandise, Dry Bulk and Liquid Bulk Sea Transport.- Determinants of the Demand of International Maritime Transport.- The Demand for Maritime Transport: A Nonlinearity and Chaos Study.- Supply.- Productivity in Maritime Transportation.- Analysis of the Returns to Scale, Elasticities of Substitution and Behavior of Shipping (General Cargo) Production.- Returns to Scale, Elasticities of Substitution and Behavior of Shipping (Dry Bulk) Transport Costs, Some Empirical Evidence.- Cycles in the Ship Building Industry: An Empirical Evidence.- Port Economic Impact.- A Methodological Discussion on Port Economic Impact Studies and Their Possible Applications to Policy Design.- An Approach to the Contribution of the Port System.- The Economic Impact of Ports: Its Importance for the Region and Also the Hinterland.- The Effect of Port Infrastructures on Regional Production.- Regulation and Economic, Technical and Allocative Efficiency.- Bootstrapped Technical Efficiency of African Seaports.- Impact of New Technology on Port Administration.- Excess Capacity, Economic Efficiency and Technical Change in a Public-Owned Port System: An Application to the Infrastructure Services of Spanish Ports.- Analysis of Technical Efficiency and Rate of Return on Investment in Ports.- Cost Benefit Analysis and Externalities.- A Cost-Benefit Analysis of a New Container Terminal.- Evaluation of Port Externalities: The Ecological Footprint of Port Authorities.

In the framework of graph transformation systems with Negative Application Conditions (NACs) the classical notion of switch equivalence of derivations is extended to permutation equivalence, because there are intuitively equivalent derivations which are not switch-equivalent if NACs are considered. By definition, two derivations are permutation-equivalent, if they respect the NACs and disregarding the NACs they are switch equivalent. A direct analysis of permutation equivalence is very complex in general, thus we propose a much more efficient analysis technique. For this purpose, we construct a Place/Transition Petri net, called dependency net, which encodes the dependencies among rule applications of the derivation, including the inhibiting effects of the NACs. The analysis of permutation equivalence is important for analysing simulation runs within development environments for systems modelled by graph transformation. The application of the technique is demonstrated by a graph transformation system within the context of workflow modelling. We show the effectiveness of the approach by comparing the minimal costs of a direct analysis with the costs of the efficient analysis applied to a derivation of our example system.

In this paper, an efficient and accurate computational method for determining responses of high-dimensional bilinear systems is developed. Predicting the dynamics of bilinear systems is computationally challenging since the piecewise-linear nonlinearity induced by contact eliminates the use of efficient linear analysis techniques. The new method, which is referred to as the hybrid symbolic-numeric computational (HSNC) method, is based on the idea that the entire nonlinear response of a bilinear system can be constructed by combining linear responses in each time interval where the system behaves linearly. The linear response in each time interval can be symbolically expressed in terms of the initial conditions. The transition time where the system switches from one linear state to the other and the displacement and velocity at the instant of transition are solved using a numerical scheme. The entire nonlinear response can then be obtained by joining each piece of the linear response together at the transition time points. The HSNC method is based on using linear features to obtain large computational savings. Both the transient and steady-state response of bilinear systems can be computed using the HSNC method. Thus, nonlinear characteristics, such as subharmonic motion, bifurcation, chaos, and multistability, can be efficiently analyzed using the HSNC method. The HSNC method is demonstrated on a single degree-of-freedom (DOF) system and a cracked cantilever beam model, and the nonlinear characteristics of these systems are examined.

Electronic computing systems are integrating modern multicore processors and GPUs aiming to perform complex software stacks in different life-critical systems, including health devices and emerging self-driving cars. Such systems are expected to experience at least one soft error per day in the near future [1], which may lead to life-threatening failures. This paper presents four novel non-intrusive fault injection techniques that enable full fault injection control and inspection of multicore systems behavior in the presence of faults. Proposed techniques were integrated into a fault injection framework and verified through a real automotive case study with up to 43 billions instructions. Results show that isolating critical application functions can lead to a more efficient soft error analysis with a reduction of masked faults in up to 28%.

A dimensionality reduction technique for efficient time series similarity analysis

Almost the entire planting of soybean commodity on Sumatra island has decreased. Mostly due to the low soybean productivity so that soybean farming is less attractive and encourages many farmers to switch to other commodities such as corn, peanuts and cassava. The low productivity of soybean is also due to limited capital to buy production facilities in the form of seeds, fertilizers and pesticides while the price of production facilities which from year to year tend to increase, especially the price of artificial fertilizers and pesticides Besides, the price of soybean during the harvest time is not stabilized and often less profitable farmers. This study aims to measure the level of economic efficiency that includes technical efficiency and allocative efficiency (price) in soybean farming in East Lampung, so it is expected to know the influence of the number of inputs used in soybean farming on the resulting output. Analytical methods used technical efficiency analysis using Data Envelopment Analysis (DEA) application, cost / allocative efficiency analysis and economic efficiency analysis. The results showed that the average technical efficiency of soybean farming system = 1 means that in general the soybean farming has reached the level of technical efficiency. The average value of the allocative efficiency of 1,4 means that on average the allocative efficiency level of oybean farming has not been achieved. The value of economic efficiency 1.4 is greater than 1. This means that maximum economic efficiency in East Lampung regency has not been achieved, therefore the use of production factors need to be added in order to achieve efficiency condition.

As a result of the development of the Functionings and Capabilities Approach to wellbeing assessment, Multidimensional Poverty and Deprivation measurement has become popular in recent years. To this end, Efficiency Analysis techniques have been borrowed from the multidimensional production theory literature and implemented in the wellbeing measurement paradigm to explore the extent of poverty in multidimensional environments. Two basic approaches prevail in the literature, a parametric isoquant estimation approach and a non-parametric data envelopment approach. This chapter outlines the two basic approaches and their origins and discusses their respective merits in their application to wellbeing measurement.

A numerically efficient technique for the analysis and design of MMIC circuits is introduced and applied to some realistic problems. The formulation is based on the method of moments (MoM) in the spatial domain, and utilizes closed-form Green's functions. Incorporating the closed-form Green's functions into the MoM has resulted in an efficient and accurate CAD algorithm. This is because use of the closed-form Green's functions not only eliminates the calculation of the spatial-domain Green's functions, but also makes it possible to evaluate the MoM matrix elements analytically We have demonstrated the application of this method here for some stripline and microstrip geometries, and compared the results with those obtained from commercial EM software, em (Sonnet Software, Inc.) © 1997 John Wiley & Sons, Inc. Int J Microwave Millimeter-Wave CAE 7: 344–358, 1997

The synthetic aperture radar (SAR) data have been used to study sea ice with respect to its motion and formation/deformation. With the prospect of the Alaska SAR Facility development in the near future, there is a great need for robust and efficient sea-ice analysis techniques. This paper presents a sea-ice motion analysis technique that can be used for (1) local motion analysis of a selected ice patch and (2) a global ice motion over the entire image area. Though there are several sea-ice motion tracking techniques, they do not provide the required operational speed or robustness. In order to meet the operational speed requirement (over fifty images per day), we have developed a sea-ice motion analysis technique which requires very little human interaction and much simplified computation. The proposed technique uses a subset of easily distinguishable features to predict global motion characteristics and apply template matching over a predicted search area. We applied the developed technique to two pairs of SEASAT SAR images, one pair with a minor motion of “ice pack” and another with a larger and discontinuous motion of “fast ice”. The two major achievements of the new approach are: first, development of a set of computer-aided tools for feature selection and registration and, secondly, implementation of an optimal search strategy for automatic template matching via a motion prediction model.

Application of Cytosense flow cytometer for the analysis of airborne bacteria collected by a high volume impingement sampler

Graphs play an important role in modern world, due to their widespread use for modeling, representing and organizing linked data. Taking into consideration that most of the “killer” applications require a graph-based representation (e.g., the Web, social network management, protein-protein interaction networks), efficient query processing and analysis techniques are required, not only because these graphs are massive but also because the operations that must be supported are complex, requiring significant computational resources. In many cases, each graph edge e is annotated by a probability value p(e), expressing its existential uncertainty. This means that with probability p(e) the edge will be present in the graph and with probability \(1-p(e)\) the edge will be absent. This gives rise to the concept of probabilistic graphs (also known as uncertain graphs). Formally, a probabilistic graph \(\mathcal{G}\) is a triplet (V, E, p) where V is the set of nodes, E is the set of edges and \(p: E \rightarrow (0,1]\). The main challenge posed by this formulation is that problems that are relatively easy to solve in exact graphs become very difficult (or even intractable) in probabilistic graphs. In this paper, we perform an overview of the algorithmic techniques proposed in the literature for uncertain graph analysis. In particular, we center our focus on the following graph mining tasks: clustering, maximal cliques, k-nearest neighbors and core decomposition. We conclude the paper with a short discussion related to distributed mining of uncertain graphs which is expected to achieve significant performance improvements.

Traditional uses, phytochemistry and pharmacology of Chios mastic gum (Pistacia lentiscus var. Chia, Anacardiaceae): A review