A unified view of the Apriori-based algorithms for frequent episode discovery

Frequent episode discovery framework is a popular framework in temporal data mining with many applications. Over the years, many different notions of frequencies of episodes have been proposed along with different algorithms for episode discovery. In this paper, we present a unified view of all the apriori-based discoverymethods for serial episodes under these different notions of frequencies. Specifically, we present a unified view of the various frequency counting algorithms. We propose a generic counting algorithm such that all current algorithms are special cases of it. This unified view allows one to gain insights into different frequencies, and we present quantitative relationships among different frequencies.Our unified view also helps in obtaining correctness proofs for various counting algorithms as we show here. It also aids in understanding and obtaining the anti-monotonicity properties satisfied by the various frequencies, the properties exploited by the candidate generation step of any apriori-based method. We also point out how our unified view of counting helps to consider generalization of the algorithm to count episodes with general partial orders.

Evolutionary Algorithm for the Determination of Optimal Mode of Bioreactor Operation

Stirred tank bioreactors, employed in the production of a variety of biologically active chemicals, are often operated in batch, fed-batch, and continuous modes of operation. The optimal design of bioreactor is dependent on the kinetics of the biological process, as well as the performance criteria (yield, productivity, etc.) under consideration. In this paper, a general framework is proposed for addressing the two key issues related to the optimal design of a bioreactor, namely, (i) choice of the best operating mode and (ii) the corresponding flow rate trajectories. The optimal bioreactor design problem is formulated with initial conditions and inlet and outlet flow rate trajectories as decision variables to maximize more than one performance criteria (yield, productivity, etc.) as objective functions. A computational methodology based on genetic algorithm approach is developed to solve this challenging multiobjective optimization problem with multiple decision variables. The applicability of the algorithm is illustrated by solving two challenging problems from the bioreactor optimization literature.

Fast algorithms for frequent episode discovery in event sequences

In this paper we consider the process of discovering frequent episodes in event sequences. The most computationally intensive part of this process is that of counting the frequencies of a set of candidate episodes. We present two new frequency counting algorithms for speeding up this part. These, referred to as non-overlapping and non-inteleaved frequency counts, are based on directly counting suitable subsets of the occurrences of an episode. Hence they are different from the frequency counts of Mannila et al [1], where they count the number of windows in which the episode occurs. Our new frequency counts offer a speed-up factor of 7 or more on real and synthetic datasets. We also show how the new frequency counts can be used when the events in episodes have time-durations as well.

A unified view of the apriori-based algorithms for frequent episode discovery

Frequent episode discovery framework is a popular framework in temporal data mining with many applications. Over the years, many different notions of frequencies of episodes have been proposed along with different algorithms for episode discovery. In this paper, we present a unified view of all the apriori-based discovery methods for serial episodes under these different notions of frequencies. Specifically, we present a unified view of the various frequency counting algorithms. We propose a generic counting algorithm such that all current algorithms are special cases of it. This unified view allows one to gain insights into different frequencies, and we present quantitative relationships among different frequencies. Our unified view also helps in obtaining correctness proofs for various counting algorithms as we show here. It also aids in understanding and obtaining the anti-monotonicity properties satisfied by the various frequencies, the properties exploited by the candidate generation step of any apriori-based method. We also point out how our unified view of counting helps to consider generalization of the algorithm to count episodes with general partial orders.

Dual-Stage Power Management Algorithms for Energy Harvesting Sensors

In this paper, we propose power management algorithms for maximizing the utility of energy harvesting sensors (EHS) that operate purely on the basis of energy harvested from the environment. In particular, we consider communication (i.e., transmission and reception) power management issues for EHS under an energy neutrality constraint. We also consider the fixed power loss effects of the circuitry, the battery inefficiency and its storage capacity, in the design of the algorithms. We propose a two-stage structure that exploits the inherent difference in the timescales at which the energy harvesting and channel fading processes evolve, without loss of optimality of the resulting solution. The outer stage schedules the power that can be used by an inner stage algorithm, so as to maximize the long term average utility and at the same time maintain energy neutrality. The inner stage optimizes the communication parameters to achieve maximum utility in the short-term, subject to the power constraint imposed by the outer stage. We optimize the algorithms for different transmission schemes such as the truncated channel inversion and retransmission strategies. The performance of the algorithms is illustrated via simulations using solar irradiance data, and for the case of Rayleigh fading channels. The results demonstrate the significant performance benefits that can be obtained using the proposed power management algorithms compared to the energy efficient (optimum when there is no storage) and the uniform power consumption (optimum when the battery has infinite capacity and is perfectly efficient) approaches.

Protein-Protein Interactions in Clathrin Vesicular Assembly: Radial Distribution of Evolutionary Constraints in Interfaces

In eukaryotic organisms clathrin-coated vesicles are instrumental in the processes of endocytosis as well as intracellular protein trafficking. Hence, it is important to understand how these vesicles have evolved across eukaryotes, to carry cargo molecules of varied shapes and sizes. The intricate nature and functional diversity of the vesicles are maintained by numerous interacting protein partners of the vesicle system. However, to delineate functionally important residues participating in protein-protein interactions of the assembly is a daunting task as there are no high-resolution structures of the intact assembly available. The two cryoEM structures closely representing intact assembly were determined at very low resolution and provide positions of C alpha atoms alone. In the present study, using the method developed by us earlier, we predict the protein-protein interface residues in clathrin assembly, taking guidance from the available low-resolution structures. The conservation status of these interfaces when investigated across eukaryotes, revealed a radial distribution of evolutionary constraints, i.e., if the members of the clathrin vesicular assembly can be imagined to be arranged in spherical manner, the cargo being at the center and clathrins being at the periphery, the detailed phylogenetic analysis of these members of the assembly indicated high-residue variation in the members of the assembly closer to the cargo while high conservation was noted in clathrins and in other proteins at the periphery of the vesicle. This points to the strategy adopted by the nature to package diverse proteins but transport them through a highly conserved mechanism.

Practical fully three-dimensional reconstruction algorithms for diffuse optical tomography

We have developed an efficient fully three-dimensional (3D) reconstruction algorithm for diffuse optical tomography (DOT). The 3D DOT, a severely ill-posed problem, is tackled through a pseudodynamic (PD) approach wherein an ordinary differential equation representing the evolution of the solution on pseudotime is integrated that bypasses an explicit inversion of the associated, ill-conditioned system matrix. One of the most computationally expensive parts of the iterative DOT algorithm, the reevaluation of the Jacobian in each of the iterations, is avoided by using the adjoint-Broyden update formula to provide low rank updates to the Jacobian. In addition, wherever feasible, we have also made the algorithm efficient by integrating along the quadratic path provided by the perturbation equation containing the Hessian. These algorithms are then proven by reconstruction, using simulated and experimental data and verifying the PD results with those from the popular Gauss-Newton scheme. The major findings of this work are as follows: (i) the PD reconstructions are comparatively artifact free, providing superior absorption coefficient maps in terms of quantitative accuracy and contrast recovery; (ii) the scaling of computation time with the dimension of the measurement set is much less steep with the Jacobian update formula in place than without it; and (iii) an increase in the data dimension, even though it renders the reconstruction problem less ill conditioned and thus provides relatively artifact-free reconstructions, does not necessarily provide better contrast property recovery. For the latter, one should also take care to uniformly distribute the measurement points, avoiding regions close to the source so that the relative strength of the derivatives for measurements away from the source does not become insignificant. (c) 2012 Optical Society of America

Applying Genetic Algorithms to Optimize Power in Tiled SNUCA Chip Multicore Architectures

We propose a novel technique for reducing the power consumed by the on-chip cache in SNUCA chip multicore platform. This is achieved by what we call a "remap table", which maps accesses to the cache banks that are as close as possible to the cores, on which the processes are scheduled. With this technique, instead of using all the available cache, we use a portion of the cache and allocate lesser cache to the application. We formulate the problem as an energy-delay (ED) minimization problem and solve it offline using a scalable genetic algorithm approach. Our experiments show up to 40% of savings in the memory sub-system power consumption and 47% savings in energy-delay product (ED).

Applying Genetic Algorithms to Optimize Power in Tiled SNUCA Chip Multicore Architectures

We propose a novel technique for reducing the power consumed by the on-chip cache in SNUCA chip multicore platform. This is achieved by what we call a "remap table", which maps accesses to the cache banks that are as close as possible to the cores, on which the processes are scheduled. With this technique, instead of using all the available cache, we use a portion of the cache and allocate lesser cache to the application. We formulate the problem as an energy-delay (ED) minimization problem and solve it offline using a scalable genetic algorithm approach. Our experiments show up to 40% of savings in the memory sub-system power consumption and 47% savings in energy-delay product (ED).