Scalable Context-Sensitive Points-To Analysis Using Multi-Dimensional Bloom Filters

Context-sensitive points-to analysis is critical for several program optimizations. However, as the number of contexts grows exponentially, storage requirements for the analysis increase tremendously for large programs, making the analysis non-scalable. We propose a scalable flow-insensitive context-sensitive inclusion-based points-to analysis that uses a specially designed multi-dimensional bloom filter to store the points-to information. Two key observations motivate our proposal: (i) points-to information (between pointer-object and between pointer-pointer) is sparse, and (ii) moving from an exact to an approximate representation of points-to information only leads to reduced precision without affecting correctness of the (may-points-to) analysis. By using an approximate representation a multi-dimensional bloom filter can significantly reduce the memory requirements with a probabilistic bound on loss in precision. Experimental evaluation on SPEC 2000 benchmarks and two large open source programs reveals that with an average storage requirement of 4MB, our approach achieves almost the same precision (98.6%) as the exact implementation. By increasing the average memory to 27MB, it achieves precision upto 99.7% for these benchmarks. Using Mod/Ref analysis as the client, we find that the client analysis is not affected that often even when there is some loss of precision in the points-to representation. We find that the NoModRef percentage is within 2% of the exact analysis while requiring 4MB (maximum 15MB) memory and less than 4 minutes on average for the points-to analysis. Another major advantage of our technique is that it allows to trade off precision for memory usage of the analysis.

Impact of Sulphate counter ion in the migration of sodium ion through soils

Laboratory advection-diffusion tests are performed on two regional soils-Brown Earth and Red Earth-in order to assess their capacity to control contaminant migration with synthetic contaminant solution of sodium sulphate with sodium concentration of 1000 mg/L. The test was designed to study the transport/attenuation behaviour of sodium in the presence of sulphate. Effective diffusion coefficient (De) that takes into consideration of attenuation processes is used. Cation exchange capacity is an important factor for the attenuation of cationic species. Monovalent sodium ion cannot usually replace other cations and the retention of sodium ion is very less. This is particularly true when chloride is anion is solution. However, sulphate is likely to play a role in the attenuation of sodium. Cation exchange capacity and type of exchangeable ions of soils are likely to play an important role. The effect of sulphate ions on the effective diffusion coefficient of sodium, in two different types of soils, of different cation exchange capacity has been studied. The effective diffusion coefficients of sodium ion for both the soils were calculated using Ogata Bank’s equation. It was shown that effective diffusion coefficient of sodium in the presence of sulphate is lower for Brown Earth than for Red Earth due to exchange of sodium with calcium ions from the exchangeable complex of clay. The soil with the higher cation exchange retained more sodium. Consequently, the breakthrough times and the number of pore volumes of sodium ion increase with the cation exchange capacity of soil.

Three-Dimensional Site Characterization Model Using Artificial Neural Networks

This research is designed to develop a new technique for site characterization in a three-dimensional domain. Site characterization is a fundamental task in geotechnical engineering practice, as well as a very challenging process, with the ultimate goal of estimating soil properties based on limited tests at any half-space subsurface point in a site.In this research, the sandy site at the Texas A&M University's National Geotechnical Experimentation Site is selected as an example to develop the new technique for site characterization, which is based on Artificial Neural Networks (ANN) technology. In this study, a sequential approach is used to demonstrate the applicability of ANN to site characterization. To verify its robustness, the proposed new technique is compared with other commonly used approaches for site characterization. In addition, an artificial site is created, wherein soil property values at any half-space point are assumed, and thus the predicted values can compare directly with their corresponding actual values, as a means of validation. Since the three-dimensional model has the capability of estimating the soil property at any location in a site, it could have many potential applications, especially in such case, wherein the soil properties within a zone are of interest rather than at a single point. Examples of soil properties of zonal interest include soil type classification and liquefaction potential evaluation. In this regard, the present study also addresses this type of applications based on a site located in Taiwan, which experienced liquefaction during the 1999 Chi-Chi, Taiwan, Earthquake.

Assessment of estrus cyclicity in the Asian elephant (Elephas maximus) by measurement of fecal progesterone metabolite 5 alpha-P-3OH, using a non-invasive assay

Reproductive management of the Asian elephant (Elephas maximus) is important for its conservation. To monitor its estrous cyclicity, we earlier used an indirect ELISA to show that levels of fecal progesterone (P(4))-metabolite (allopregnanolone: 5 alpha-P-3OH) in semi-captive females sampled randomly positively correlated with serum P(4) levels [12]. In this longitudinal study (51 weeks), we measured levels of fecal 5 alpha-P-3OH and serum P(4) in seven semi-captive female elephants. Females exhibited three types of hormonal profiles. Four females showed cyclical patterns of fecal 5 alpha-P-3OH and serum P(4) typical of normal estrous cycles, two showed acyclic pattern while one showed high values indicative of a pregnant animal. Values for anestrous or follicular phases were <= 0.3 mu g g(-1), (5 alpha-P-3OH) and <= 0.3 ng mL(-1) (P(4)); for luteal phase 0.32-11.09 mu g g(-1) (5 alpha-P-3OH) and 0.32-1.48 ng mL(-1) (P(4)); for pregnancy 1.41-7.38 mu g g(-1) (5 alpha-P-3OH) and 0.39-1.6 ng mL(-1) (R(4)). A positive correlation (t = 8.8, p < 0.01, n = 321) between levels of fecal 5 alpha-P-3OH and serum P4 was observed. A random sample of 30 free-ranging female elephants showed fecal 5 alpha-P-3OH values of 0.06-23.4 mu g g(-1), indicating them to be in different stages of estrous cyclicity. This study is the first to assess the reproductive phases of female Asian elephants based on the correlative-patterns of both the fecal 5 alpha-P-3OH and serum P(4) values over multiple estrous cycles. This has a potential application in the reproductive management and conservation of Asian elephants. (C) 2011 Elsevier Inc. All rights reserved.

Minimal unsatisfiable sets: Classification and bounds

Proving the unsatisfiability of propositional Boolean formulas has applications in a wide range of fields. Minimal Unsatisfiable Sets (MUS) are signatures of the property of unsatisfiability in formulas and our understanding of these signatures can be very helpful in answering various algorithmic and structural questions relating to unsatisfiability. In this paper, we explore some combinatorial properties of MUS and use them to devise a classification scheme for MUS. We also derive bounds on the sizes of MUS in Horn, 2-SAT and 3-SAT formulas.

Learning polyhedral classifiers using logistic function

In this paper we propose a new algorithm for learning polyhedral classifiers. In contrast to existing methods for learning polyhedral classifier which solve a constrained optimization problem, our method solves an unconstrained optimization problem. Our method is based on a logistic function based model for the posterior probability function. We propose an alternating optimization algorithm, namely, SPLA1 (Single Polyhedral Learning Algorithm1) which maximizes the loglikelihood of the training data to learn the parameters. We also extend our method to make it independent of any user specified parameter (e.g., number of hyperplanes required to form a polyhedral set) in SPLA2. We show the effectiveness of our approach with experiments on various synthetic and real world datasets and compare our approach with a standard decision tree method (OC1) and a constrained optimization based method for learning polyhedral sets.

Monsoon Regimes and Processes in CCSM4. Part I: The Asian-Australian Monsoon

The simulation characteristics of the Asian-Australian monsoon are documented for the Community Climate System Model, version 4 (CCSM4). This is the first part of a two part series examining monsoon regimes in the global tropics in the CCSM4. Comparisons are made to an Atmospheric Model Intercomparison Project (AMIP) simulation of the atmospheric component in CCSM4 Community Atmosphere Model, version 4, (CAM4)] to deduce differences in the monsoon simulations run with observed sea surface temperatures (SSTs) and with ocean-atmosphere coupling. These simulations are also compared to a previous version of the model (CCSM3) to evaluate progress. In general, monsoon rainfall is too heavy in the uncoupled AMIP run with CAM4, and monsoon rainfall amounts are generally better simulated with ocean coupling in CCSM4. Most aspects of the Asian-Australian monsoon simulations are improved in CCSM4 compared to CCSM3. There is a reduction of the systematic error of rainfall over the tropical Indian Ocean for the South Asian monsoon, and well-simulated connections between SSTs in the Bay of Bengal and regional South Asian monsoon precipitation. The pattern of rainfall in the Australian monsoon is closer to observations in part because of contributions from the improvements of the Indonesian Throughflow and diapycnal diffusion in CCSM4. Intraseasonal variability of the Asian-Australian monsoon is much improved in CCSM4 compared to CCSM3 both in terms of eastward and northward propagation characteristics, though it is still somewhat weaker than observed. An improved simulation of El Nino in CCSM4 contributes to more realistic connections between the Asian-Australian monsoon and El Nino-Southern Oscillation (ENSO), though there is considerable decadal and century time scale variability of the strength of the monsoon-ENSO connection.