Berger-Coburn-Lebow representation for pure isometric representations of product system over N02\mathbb N^2_0

We obtain Berger-Coburn-Lebow (BCL)-representation for pure isometric covariant representation of product system over $\mathbb{N}_0^2$. Then the corresponding complete set of (joint) unitary invariants is studied, and the BCL- representations are compared with other canonical multi-analytic descriptions of the pure isometric covariant representation. We characterize the invariant subspaces for the pure isometric covariant representation. Also, we study the connection between the joint defect operators and Fringe operators, and the Fredholm index is introduced in this case. Finally, we introduce the notion of congruence relation to classify the isometric covariant representations of the product system over $\mathbb{N}_0^2$.Comment: 28 page

A characterization of invariant subspaces for isometric representations of product system over N0k\mathbb{N}_0^{k}

Using the Wold-von Neumann decomposition for the isometric covariant representations due to Muhly and Solel, we prove an explicit representation of the commutant of a doubly commuting pure isometric representation of the product system over $\mathbb{N}_0^{k}.$ As an application, we study a complete characterization of invariant subspaces for a doubly commuting pure isometric representation of the product system. This provides us a complete set of isomorphic invariants. Finally, we classify a large class of commuting isometric representations of the product system.Comment: 20 page

Comparison in Thermal Conductivity of Hollow Concrete blocks filled with Straw Bales & Tyre Waste

The thermal conductivity of straw bales is an intensively discussed topic in the international straw bale community. Straw bales are, by nature, highly heterogeneous and porous. They can have a relatively large range of density and the baling process can influence the way the fibers are organized within the bale. In addition, straw bales have a larger thickness than most of the insulating materials that can be found in the building industry. Measurement apparatus is usually not designed for such thicknesses, and most of the thermal conductivity values that can be found in the literature are defined based on samples in which the straw bales are resized. During this operation, the orientation of the fibers and the density may not be preserved. This paper starts with a literature review of straw bale thermal conductivity measurements and presents a measuring campaign performed with a specific Guarded Hot Plate, designed to measure samples up to 40 cm thick. The influence of the density is discussed thoroughly. Representative values are proposed for a large range of straw bales to support straw-bale development in the building industry This paper comprises the tests performed to determine the thermal conductivity of hollow concrete blocks using straw bales filled in hollow concrete block. The purpose of this study is to examine the possibility of using straw bales in hollow concrete block. The straw bales were used to make concrete block in the masonry units. This study examines the thermal behavior of concrete construction elements (bricks, slabs) made filled with different amounts of straw bales particles (80%, 70% and 60%) according with different thickness of concrete. Once the bricks, slabs were obtained, three different closed test cells were built and subjected to several heating/cooling periods. By recording the temperature difference between inside and outside the wall of concrete block, it was found that the thermal behavior depend on the filling percentage of tyre waste particles. This study is based on the human and atmospheric comfort in building structures in different environment condition

Subcellular localization for Gram Positive and Gram Negative Bacterial Proteins using Linear Interpolation Smoothing Model

Protein subcellular localization is an important topic in proteomics since it is related to a proteins overall function, help in the understanding of metabolic pathways, and in drug design and discovery. In this paper, a basic approximation technique from natural language processing called the linear interpolation smoothing model is applied for predicting protein subcellular localizations. The proposed approach extracts features from syntactical information in protein sequences to build probabilistic profiles using dependency models, which are used in linear interpolation to determine how likely is a sequence to belong to a particular subcellular location. This technique builds a statistical model based on maximum likelihood. It is able to deal effectively with high dimensionality that hinder other traditional classifiers such as Support Vector Machines or k-Nearest Neighbours without sacrificing performance. This approach has been evaluated by predicting subcellular localizations of Gram positive and Gram negative bacterial proteins

Protein fold recognition using genetic algorithm optimized voting scheme and profile bigram

In biology, identifying the tertiary structure of a protein helps determine its functions. A step towards tertiary structure identification is predicting a protein’s fold. Computational methods have been applied to determine a protein’s fold by assembling information from its structural, physicochemical and/or evolutionary properties. It has been shown that evolutionary information helps improve prediction accuracy. In this study, a scheme is proposed that uses the genetic algorithm (GA) to optimize a weighted voting scheme to improve protein fold recognition. This scheme incorporates k-separated bigram transition probabilities for feature extraction, which are based on the Position Specific Scoring Matrix (PSSM). A set of SVM classifiers are used for initial classification, whereupon their predictions are consolidated using the optimized weighted voting scheme. This scheme has been demonstrated on the Ding and Dubchak (DD), Extended Ding and Dubchak (EDD) and Taguchi and Gromhia (TG) datasets benchmarked data sets

Towards Optimizing Storage Costs on the Cloud

We study the problem of optimizing data storage and access costs on the cloud while ensuring that the desired performance or latency is unaffected. We first propose an optimizer that optimizes the data placement tier (on the cloud) and the choice of compression schemes to apply, for given data partitions with temporal access predictions. Secondly, we propose a model to learn the compression performance of multiple algorithms across data partitions in different formats to generate compression performance predictions on the fly, as inputs to the optimizer. Thirdly, we propose to approach the data partitioning problem fundamentally differently than the current default in most data lakes where partitioning is in the form of ingestion batches. We propose access pattern aware data partitioning and formulate an optimization problem that optimizes the size and reading costs of partitions subject to access patterns. We study the various optimization problems theoretically as well as empirically, and provide theoretical bounds as well as hardness results. We propose a unified pipeline of cost minimization, called SCOPe that combines the different modules. We extensively compare the performance of our methods with related baselines from the literature on TPC-H data as well as enterprise datasets (ranging from GB to PB in volume) and show that SCOPe substantially improves over the baselines. We show significant cost savings compared to platform baselines, of the order of 50% to 83% on enterprise Data Lake datasets that range from terabytes to petabytes in volume.Comment: The first two authors contributed equally. 12 pages, Accepted to the International Conference on Data Engineering (ICDE) 202

Gene masking - a technique to improve accuracy for cancer classification with high dimensionality in microarray data

Background: High dimensional feature space generally degrades classification in several applications. In this paper, we propose a strategy called gene masking, in which non-contributing dimensions are heuristically removed from the data to improve classification accuracy. Methods: Gene masking is implemented via a binary encoded genetic algorithm that can be integrated seamlessly with classifiers during the training phase of classification to perform feature selection. It can also be used to discriminate between features that contribute most to the classification, thereby, allowing researchers to isolate features that may have special significance. Results: This technique was applied on publicly available datasets whereby it substantially reduced the number of features used for classification while maintaining high accuracies. Conclusion: The proposed technique can be extremely useful in feature selection as it heuristically removes non-contributing features to improve the performance of classifiers