Experience with Applying Formal Methods to Protocol Specification and System Architecture

In the last three years or so we at Enterprise Platforms Group at Intel Corporation have been applying formal methods to various problems that arose during the process of defining platform architectures for Intel's processor families. In this paper we give an overview of some of the problems we have worked on, the results we have obtained, and the lessons we have learned. The last topic is addressed mainly from the perspective of platform architects.     

Conceptual aspects of geometric quantum computation

Geometric quantum computation is the idea that geometric phases can be used to implement quantum gates, i.e., the basic elements of the Boolean network that forms a quantum computer. Although originally thought to be limited to adiabatic evolution, controlled by slowly changing parameters, this form of quantum computation can as well be realized at high speed by using nonadiabatic schemes. Recent advances in quantum gate technology have allowed for experimental demonstrations of different types of geometric gates in adiabatic and nonadiabatic evolution. Here, we address some conceptual issues that arise in the realizations of geometric gates. We examine the appearance of dynamical phases in quantum evolution and point out that not all dynamical phases need to be compensated for in geometric quantum computation. We delineate the relation between Abelian and non-Abelian geometric gates and find an explicit physical example where the two types of gates coincide. We identify differences and similarities between adiabatic and nonadiabatic realizations of quantum computation based on non-Abelian geometric phases.     

Adaptive neuro-fuzzy inference system multi-objective optimization using the genetic algorithm/singular value decomposition method for modelling the discharge coefficient in rectangular sharp-crested side weirs

In the present article, the adaptive neuro-fuzzy inference system (ANFIS) is employed to model the discharge coefficient in rectangular sharp-crested side weirs. The genetic algorithm (GA) is used for the optimum selection of membership functions, while the singular value decomposition (SVD) method helps in computing the linear parameters of the ANFIS results section (GA/SVD-ANFIS). The effect of each dimensionless parameter on discharge coefficient prediction is examined in five different models to conduct sensitivity analysis by applying the above-mentioned dimensionless parameters. Two different sets of experimental data are utilized to examine the models and obtain the best model. The study results indicate that the model designed through GA/SVD-ANFIS predicts the discharge coefficient with a good level of accuracy (mean absolute percentage error?=?3.362 and root mean square error?=?0.027). Moreover, comparing this method with existing equations and the multi-layer perceptron–artificial neural network (MLP-ANN) indicates that the GA/SVD-ANFIS method has superior performance in simulating the discharge coefficient of side weirs.     

A magnetic bead-based DNA extraction and purification microfluidic device

This article introduces a novel magnetic bead-based DNA extraction and purification device using active magnetic mixing approach. Mixing and separation steps are performed using functionalised superparamagnetic beads suspended in cell lysis buffer in a circular chamber that is sandwiched between two external magnetic coils. Non-uniform nature of magnetic field causes temporal and spatial distribution of beads within the chamber. This process efficiently mixes the lysis buffer and whole blood in order to extract DNA from target cells. Functionalized surface of the magnetic beads then attract the exposed DNA molecules. Finally, DNA-attached magnetic beads are attracted to the bottom of the chamber by activating the bottom magnetic coil. DNA molecules are extracted from magnetic beads by washing and re-suspension processes. In this study, a circular PMMA microchamber, 25 μL in volume, 500 μm in depth and 8 mm in diameter was fabricated to purify DNA from spiked bacterial cell cultures into the whole blood sample using Promega Magazorb DNA extraction kit. The lysis efficiency was evaluated using a panel of Gram-positive (Bacillus subtilis) and Gram-negative (Escherichia coli) bacterial cells cultures into the blood sample to achieve approximately 100,000 copy levels inside the chip. Manufacturer’s standard extraction protocol was modified to a more simplified process suitable for chip-based extraction. The lysis step was performed using 5 min incubation at 56 °C followed by 5 min incubation at room temperature for binding process. Temperature rise was generated and maintained by the same external magnetic coils used for active mixing. The yield/purity and recovery levels of the extracted DNA were evaluated using quantitative UV spectrophotometer and real-time PCR assay, respectively. Real-time PCR results indicated efficient chip-based bacterial DNA extraction using modified extraction protocol comparable to the standard bench-top extraction process.     

An enhanced beam search algorithm for the Shortest Common Supersequence Problem

The Shortest Common Supersequence Problem asks to obtain a shortest string that is a supersequence of every member of a given set of strings. It has applications, among others, in data compression and oligonucleotide microarray production. The problem is NP-hard, and the existing exact solutions are impractical for large instances. In this paper, a new beam search algorithm is proposed for the problem, which employs a probabilistic heuristic and uses the dominance property to further prune the search space. The proposed algorithm is compared with three recent algorithms proposed for the problem on both random and biological sequences, outperforming them all by quickly providing solutions of higher average quality in all the experimental cases. The Java source and binary files of the proposed IBS_SCS algorithm and our implementation of the DR algorithm and all the random and real datasets used in this paper are freely available upon request.     

Quantifying the size‐resolved dynamics of indoor bioaerosol transport and control

Understanding the bioaerosol dynamics of droplets and droplet nuclei emitted during respiratory activities is important for understanding how infectious diseases are transmitted and potentially controlled. To this end, we conducted experiments to quantify the size‐resolved dynamics of indoor bioaerosol transport and control in an unoccupied apartment unit operating under four different HVAC particle filtration conditions. Two model organisms (Escherichia coli K12 and bacteriophage T4) were aerosolized under alternating low and high flow rates to roughly represent constant breathing and periodic coughing. Size‐resolved aerosol sampling and settle plate swabbing were conducted in multiple locations. Samples were analyzed by DNA extraction and quantitative polymerase chain reaction (qPCR). DNA from both organisms was detected during all test conditions in all air samples up to 7 m away from the source, but decreased in magnitude with the distance from the source. A greater fraction of T4 DNA was recovered from the aerosol size fractions smaller than 1 μm than E. coli K12 at all air sampling locations. Higher efficiency HVAC filtration also reduced the amount of DNA recovered in air samples and on settle plates located 3‐7 m from the source.     

Ab-initio study of electronic and elastic properties of Mg(BH4)(NH2) complex hydride

Structural and electronic properties of complex hydride Mg(BH₄)(NH₂), applicable in hydrogen storage, were studied in framework of pseudopotential-density functional theory. This compound shows an indirect band gap of 4.95 eV which is categorized in insulator materials. The calculated total and partial density of states of this compound show that the hydrogen bonding in BH₄ anion is mainly covalent and the hydrogen-hydrogen repulsion is more in anion BH₄ than in anion NH₂. The calculated bulk modulus from Birch-Murnaghan equation of state is in close agreement with that obtained from the elastic constants. The obtained bulk modulus (19.27 GPa) shows this compound is more ductile than binary and ternary hydrides and it does not have a brittle structure. Therefore, it is a good candidate for hydrogenation and dehydrogenation cycles. The stability of the structure in ambient pressure is also declared by calculating the elastic coefficients. However, the existence of elastic anisotropy in the compound demonstrates the less compressibility of it along the c axis than a and b axes. The small amount of Poisson ratio indicates that it is more stable against shear compared to common borohydrides. This point is important for hydride stability in hydrogenation and dehydrogenation cycles.     

Predicting Survival Outcomes in the Presence of Unlabeled Data

Many clinical studies require the follow-up of patients over time. This is challenging: apart from frequently observed drop-out, there are often also organizational and financial challenges, which can lead to reduced data collection and, in turn, can complicate subsequent analyses. In contrast, there is often plenty of baseline data available of patients with similar characteristics and background information, e.g., from patients that fall outside the study time window. In this article, we investigate whether we can benefit from the inclusion of such unlabeled data instances to predict accurate survival times. In other words, we introduce a third level of supervision in the context of survival analysis, apart from fully observed and censored instances, we also include unlabeled instances. We propose three approaches to deal with this novel setting and provide an empirical comparison over fifteen real-life clinical and gene expression survival datasets. Our results demonstrate that all approaches are able to increase the predictive performance over independent test data. We also show that integrating the partial supervision provided by censored data in a semi-supervised wrapper approach generally provides the best results, often achieving high improvements, compared to not using unlabeled data.