Performance of hashed cache data migration schemes on multicomputers

Many researchers approach the problem of programming distributed memory machines by assuming a global shared name space. Thus the user views the distributed memory of the machine as though it were shared. A major issue that arises at this point is how to manage the memory. When a processor accesses data stored on another processor's memory, data must be moved between the two processors. Once these data are retrieved from another processor's memory, several interesting issues are raised. Where should these data be stored locally? What transformations must be performed to the code to guarantee that the nonlocal accesses reference the correct memory location? What optimizations can be performed to reduce the time spent in accessing the nonlocal data? In this paper we examine various data migration mechanisms that allow an explicit and controlled mapping of data to memory. We describe, experimentally evaluate, and model a set of schemes for storing and retrieving off-processor array elements. The schemes are all based on using hash tables for efficient access of nonlocal data. The three different techniques evaluated are the basic hashed cache, partial enumeration, and full enumeration, the details of which are described in the paper. In all three schemes, nonlocal data are stored in hash tables—the difference is in the amount of memory used by the schemes and the retrieval mechanisms for nonlocal data.     

Fraudulent Firm Classification: A Case Study of an External Audit

This paper is a case study of visiting an external audit company to explore the usefulness of machine learning algorithms for improving the quality of an audit work. Annual data of 777 firms from 14 different sectors are collected. The Particle Swarm Optimization (PSO) algorithm is used as a feature selection method. Ten different state-of-the-art classification models are compared in terms of their accuracy, error rate, sensitivity, specificity, F measures, Mathew’s Correlation Coefficient (MCC), Type-I error, Type-II error, and Area Under the Curve (AUC) using Multi-Criteria Decision-Making methods like Simple Additive Weighting (SAW) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The results of Bayes Net and J48 demonstrate an accuracy of 93% for suspicious firm classification. With the appearance of tremendous growth of financial fraud cases, machine learning will play a big part in improving the quality of an audit field work in the future.     

Unknown input modeling and robust fault diagnosis using black box observers

A key issue that needs to be addressed while performing fault diagnosis using black box models is that of robustness against abrupt changes in unknown inputs. A fundamental difficulty with the robust FDI design approaches available in the literature is that they require some a priori knowledge of the model for unmeasured disturbances or modeling uncertainty. In this work, we propose a novel approach for modeling abrupt changes in unmeasured disturbances when innovation form of state space model (i.e. black box observer) is used for fault diagnosis. A disturbance coupling matrix is developed using singular value decomposition of the extended observability matrix and further used to formulate a robust fault diagnosis scheme based on generalized likelihood ratio test. The proposed modeling approach does not require any a priori knowledge of how these faults affect the system dynamics. To isolate sensor and actuator biases from step jumps in unmeasured disturbances, a statistically rigorous method is developed for distinguishing between faults modeled using different number of parameters. Simulation studies on a heavy oil fractionator example show that the proposed FDI methodology based on identified models can be used to effectively distinguish between sensor biases, actuator biases and other soft faults caused by changes in unmeasured disturbance variables. The fault tolerant control scheme, which makes use of the proposed robust FDI methodology, gives significantly better control performance than conventional controllers when soft faults occur. The experimental evaluation of the proposed FDI methodology on a laboratory scale stirred tank temperature control set-up corroborates these conclusions.     

Degradable blends of semi-crystalline and amorphous branched poly(caprolactone): Effect of microstructure on blend properties

The blend properties of semi-crystalline and amorphous polycaprolactone (PCL) are shown here for the first time. The amorphous polycaprolactone (PCLB) having 20% branches was made by radical-ring-opening polymerization of 2-methylene-1,3-dioxepane. The presence of branches led to non-crystallinity in PCLB. The blends were made in solution in entire concentration range and showed some very interesting properties in terms of synergistic effect on crystallinity leading to the increased crystallinity of PCL and induced crystallinity of amorphous PCLB, minima in the % elongation and maxima in spherulite size depending upon the ratio of amorphous and crystalline part. There was a drastic increase in the compostability as compared to pure PCL on adding even low % of amorphous PCLB in the blends without affecting the thermal stability. The addition of PCLB also affected the transparency and it increased exponentially from about 8% at 650 nm for PCL to about 45% for a blend with composition PCL:PCLB 30:70.     

Study of Thermally Activated Phase-Slip and Scaling of Magneto-Resistance Near TC in YBa2Cu3O7−δ/Ag bulk composites

Journal of Superconductivity and Novel Magnetism - Dissipation in YBCO polycrystalline bulk samples added with nominally different amounts of silver (0, 5, 15, 25 wt%) has been studied through...     

Multi-level Gaussian mixture modeling for detection of malicious network traffic

The Journal of Supercomputing - Along with the growing network connectivity across the world, there is a substantial increase in malicious network traffic to exploit the vulnerabilities, thus...     

Effect of Disorder on Electronic,Magnetic, and Optical Properties of Co2CrZ Heusler Alloys (Z = Al,Ga, Si,Ge)

This paper presents the effect of disorder on electronic, magnetic, and optical properties of Co₂CrZ (Z = Al, Si, Ga, Ge) Heusler alloy using density functional theory. Binary mixing is the most common form of atomic disorder in these compounds. We have considered three types of disorders: DO ₃, A2, and B2 disorder which corresponds to X-Y, X-Z, and Y-Z mixing, respectively. After structural optimization, we found that A2 disorder has high formation energy and is most unlikely to occur. The half-metallic nature of the alloy is destroyed in the presence of DO ₃ and A2 disorder. The destruction of half-metallicity is due to reconstruction of energy states. B2 disorder retains the half-metallic nature of the alloy but spin-polarization value is reduced slightly as compared to the ordered alloy. In addition, the optical properties such as dielectric function, refractive index, absorption spectra, optical conductivity, reflectivity, and energy loss function of these alloys have also been investigated.     

Analysis of Acquired Indoor LTE-A Data from an Actual HetNet Cellular Deployment

Wireless Personal Communications - Increasing the user downlink throughput is always a task of utmost importance for any cellular network service provider. However, most of the research is only...     

Star-anise (Illicium verum) and black caraway (Carum nigrum) as natural antioxidants

The various solvent fractions of star-anise (Illicium verum) and black caraway (Carum nigrum), along with their spice powders and volatile oils, were prepared and evaluated for antioxygenic activity, using different methods. Star-anise powder and its ethanol/water (80:20)-soluble fraction showed strong antioxygenic activity in refined sunflower oil while the petroleum ether fraction exhibited marginal antioxygenic activity and the water-soluble fraction was practically devoid of any activity in sunflower oil. The black caraway powder showed marginal antioxygenic activity while its ethanol/water fraction (80:20) showed strong antioxygenic activity and all other fractions showed slight pro-oxygenic activity in refined sunflower oil. Both the spice powders and their extracts were also evaluated for antioxidant activity by linoleic acid peroxidaton, β-carotene-linoleate and 1, 1-diphenyl-2-picryl hydrazyl (DPPH) methods. Both the star-anise and black caraway powders, as well as their ethanol/water extracts, exhibited strong antioxygenic activity. Volatile oils from both the spices exhibited antioxygenic activity and the activity did not seem to be concentration-dependent. Volatile oils from star-anise showed relatively higher antioxygenic activity than did those from black caraway. Gas chromatography–mass spectroscopy (GC–MS) studies on star-anise and black caraway volatile oils resulted in the identification of 25 and 22 compounds, respectively, representing 94–97% of the total content.