Macro Operations Research Articles

CONCEPT OF BUILDING A LIBRARY OF TASKS AND SOLUTIONS

The article develops the concept of building a library of tasks and their solutions in the form of computational processes (CPs) and Petri nets (PNs) with the preliminary transformation of CPs into PNs. The conversion includes the extraction of macro operations (MOs) from CPs as a result of their analysis, the construction of PNs and their fragments as models of CPs and MOs, and the modeling of PNs and their fragments. The architecture, structure and interface of the library are proposed. Computational process is a sequence of time-ordered operations and procedures of varying degrees of complexity. The set of PNs, as well as the set of problems for which PNs are solutions, is divided into classes and subclasses. A class includes problems such as Combinatorial, Linear Algebraic Equations, etc.; the Combinatorial class includes subclasses of problems such as Sorting, Search, Sets, Graphs, etc. A macro operation is a fragment of a GP that is a functionally complete computational construct. MOs can have different levels of complexity, starting with the elementary one; they can be built hierarchically. A Petri net is a bipartite graph with two interconnected objects: event-condition, process-resource, etc. Fragments of the PN are models of the MO. The components of the library are tree nodes supplemented with files of their verbal descriptions built according to certain rules. The architecture, structure, interface, and operations of the library provide convenient management, editing, and expansion of knowledge about computational processes and macro operations, making the library open for further research and development by the developer. All files are available to the user, but without the right to edit. The paper presents an example of the initial filling of the library for the Sorting task.

Reduced-Complexity Algorithms for Tessarine Neural Networks.

The brief presents the results of synthesizing efficient algorithms for implementing the basic data-processing macro operations used in tessarine-valued neural networks. These macro operations primarily include the macro operation of multiplication of two tessarines: the macro operation of calculating the inner product of two tessarine-valued vectors and the macro operation of multiple multiplications of one tessarine by the set of different tessarines. When synthesizing the discussed algorithms, we use the fact that tessarine multiplications can be interpreted as matrix-vector products. In each of these cases, the matrices have a specific block structure, which allows them to be efficiently factorized. This factorization provides a reduction in the multiplicative complexity of computing the product of two tessarines. In what follows, we use the optimized tessarine multiplication algorithm to synthesize reduced-complexity algorithms for the inner product of two tessarine-valued vectors as well as to compute multiple tessarine multiplication. Also, to further decrease the computational complexity of the inner product of two tessarine-valued vectors and multiple tessarine multiplication, we take into account that the selective sets of operations in the computations of all partial matrix-vector multiplications participating in these macro operations are the same. Accounting for this fact provides an additional reduction in computation complexity.

OM-NAS: pigmented skin lesion image classification based on a neural architecture search.

Because pigmented skin lesion image classification based on manually designed convolutional neural networks (CNNs) requires abundant experience in neural network design and considerable parameter tuning, we proposed the macro operation mutation-based neural architecture search (OM-NAS) approach in order to automatically build a CNN for image classification of pigmented skin lesions. We first used an improved search space that was oriented toward cells and contained micro and macro operations. The macro operations include InceptionV1, Fire and other well-designed neural network modules. During the search process, an evolutionary algorithm based on macro operation mutation was employed to iteratively change the operation type and connection mode of parent cells so that the macro operation was inserted into the child cell similar to the injection of virus into host DNA. Ultimately, the searched best cells were stacked to build a CNN for the image classification of pigmented skin lesions, which was then assessed on the HAM10000 and ISIC2017 datasets. The test results showed that the CNN built with this approach was more accurate than or almost as accurate as state-of-the-art (SOTA) approaches such as AmoebaNet, InceptionV3 + Attention and ARL-CNN in terms of image classification. The average sensitivity of this method on the HAM10000 and ISIC2017 datasets was 72.4% and 58.5%, respectively.

Analysis of the intensity distribution in the longitudinal section of a focused laser beam using the Zemax programming language

Subject of study. We present a computer simulation of laser radiation propagation using the Zemax program. Aim of study. We develop a macro written in the Zemax programming language designed to obtain the intensity distribution in the longitudinal section of a focused laser beam. Method. The developed macro uses Zemax functions based on integral transformations, making it possible to obtain the results of the modeling of radiation propagation through optical systems. Main results. A macro for analyzing the intensity distribution in the longitudinal section of a laser beam has been developed using the Zemax programming language. The presented macro is used to analyze the propagation of a laser beam affected by different aberrational distortions. The results of the macro operation and their analysis are presented. Practical significance. The proposed macro expands the standard capabilities of Zemax and can be used for analyzing laser radiation propagation. The presented results of the macro demonstrate the possibility of its application in analyzing the effect of various aberrational distortions on the parameters of a laser beam focused in the bulk of a transparent material subjected to laser processing. Therefore, the macro can facilitate the control of the spatial distribution of intensity in a focused laser beam with high accuracy to ensure maximum processing efficiency of the material being modified.

ПРЕОБРАЗОВАНИЕ ПОСЛЕДОВАТЕЛЬНОГО ИНФОРМАЦИОННОГО ГРАФА МЕТОДА ПРОГОНКИ В ПАРАЛЛЕЛЬНУЮ ФОРМУ

Many computational tasks can be represented in the form of a sequential information graph.In the general case, such an information graph cannot be reduced to a parallel form in order tospeed up the execution of its operations. But if the vertices of this graph have the properties ofassociativity, distributivity, etc., such a graph can be transformed into a parallel-pipeline form.These transformations can be performed not only on graphs containing elementary operations -addition, multiplication, logical AND, etc. - but also over graphs containing macro operations.One example of such graphs is the information graph for solving SLAEs by the sweep method(Thomas's method). The article considers a solution for tridiagonal linear systems. The informationgraph of the sweep method consists of two parts: the forward move, in which the transitionfrom the three-diagonal form to the two-diagonal form is performed, and the reverse move, inwhich the values of the variables are directly calculated. Despite the fact that the operations thatmake up the basic macro-operation of the sweep method have the property of associativity, a simpletransformation of the graph to a pyramidal form will not give the desired result. It is necessaryto transform the basic macro operations in a special way and change what data is received onthem. After that, it will be possible to bring the graph to a pyramidal form. For the reverse move, asimilar transformation of the graph and its constituent base subgraphs is applied. Since in order tostart computations in the reverse run, we need to complete the computations of the forward run,we should switch from two specialized types of computational blocks to one universal one, andbuild a universal computational structure on its basis.

COMPUTATIONAL ASSISTANCE: newsbot development for financial organizations

This research aims to develop the Minimum Viable Product (MVP) of a cloud platform that transforms structured data into automated text, to enhance the production of content in Brazilian stock brokerage companies. It is based on applied research that combines different techniques for the development of MVP. As a theoretical path to support product development, it permeates strategic communication in corporations, as well as the adoption of algorithms and content automation in journalistic production. Among the results achieved, the work presents the logic of infrastructure of macro operation for the newsbot and the main elements for the many layers of development that an application of this nature needs.

Preliminary Study on the Realization and Teaching Practice of Digital Teaching Game in Genetic Engineering Experiment

Aiming at various problems in genetic engineering experimental learning, an attempt was made to fuse the elements of “gamification” with virtual technology to develop genetic engineering experimental teaching software “genetic engineering game experiment (GEGE)”. Compared with the virtual experimental platform, GEGE software focuses on the educational function of training students to mobilize higher-order thinking to solve problems in a game-breaking way. The teaching practice results show that the GEGE software has played a positive role in helping learners to establish a connection between macro operations and micro results, promoting imagination, and enhancing the use of knowledge. This attempt provides ideas for promoting digital game teaching in higher education biology majors.

Effects of Temperature on the Characteristics of Nitrogen Removal and Microbial Community in Post Solid-Phase Denitrification Biofilter Process.

In order to solve the problems of high energy consumption, complex process and low nitrogen removal efficiency in the currently available low carbon source wastewater treatment processes, a novel coagulation sedimentation/post-solid-phase denitrification biofilter process (CS-BAF-SPDB) was proposed. The effect of temperature on the nitrogen removal performance of BAF-SPDB was intensively studied, and the mechanism of the effect of temperature on nitrogen removal performance was analyzed from the perspective of microbial community structure by using the polymerase chain reaction denaturing gradient gel electrophoresis (PCR-DGGE). The results showed that, to realize favorable nitrifying and denitrifying performance simultaneously in the BAF-SPDB unit, the operation temperature should be set above 18 °C. In addition, the influence of the macro operation parameters on the performance of the BAF and SPDB has a direct relationship with the dynamic changes of the micro microbial community. The influence of temperature on nitrification performance in BAF was mainly embodied in the change of composition, amount and activity of ammonia oxidizing bacteria Candidatus Nitrospira defluvii and nitrite oxidizing bacteria Nitrosomonas sp. Nm47, while that on denitrification performance in SPDB is mainly embodied in the change of composition and amount of solid carbon substrate degrading denitrifying bacteria Pseudomonas sp., Myxobacterium AT3-03 and heterotrophic denitrifying bacteria Dechloromonas agitate, Thauera aminoaromatica, Comamonas granuli and Rubrivivax gelatinosus.

FORMALIZED OBTAINING OF THE COMMUNICATION OPERATIONS OF GRAINED PARALLEL ALGORITHMS

Algorithms designed for implementation on parallel computers with distributed memory consist of computational macro operations (calculation grains) and communication operations specifying the data arrays exchange between computing nodes. The major difficulty is how to find an efficient way to organize the data exchange. To solve this problem, it is first necessary to identify information dependences between macro operations and then to generate the communication operations caused by these dependences. To automate and simplify the process of code generation, it is necessary to formalize communication operations. The formalization is known for the case of homogeneous information dependences. Such formalization uses the vectors of global dependences as a representation of dependences between the calculation grains. Also, there is a way that makes it possible to obtain the data arrays exchange, but it requires the usage of tools to work with polyhedra and does not formalize communication operations. This article presents a formalization method and a method of inclusion of communication operations into the algorithm structure (receiving and sending data arrays) in case of a parallel algorithm with affine dependences. The usage of functions determining the relationship between macro operations allowed obtaining explicit representations of communication operations. This work is a generalization of the formalization of the operations of sending data in a parallel algorithm, where operations are not divided into macro operations, as well as a generalization of some aspects of obtaining the communication operation method.

DMFA-based operation model for fermentation processes

The existing dynamical fermentation models only reveal approximate macroscopic properties involving no internal mechanism, while dynamic metabolic flux analysis (DMFA) provides internal microscopic dynamic features, but without regulation from external operation conditions. This is the first attempt to bridge the mapping between macro operation variables and micro metabolic fluxes. Based on the macro-micro mapping relationship, a new operation model was constructed, which can use macro operation variables to regulate micro metabolic fluxes. Firstly, metabolic network was analyzed based on DMFA to derive flux distribution. Next, the fluxes defined as outputs were related to macro operation variables to establish the operation model. The complexity of cellular growth and diversity of flux distribution led to nonlinear and multi-stage characteristics of the mapping relationship, and thus a multi-model modeling method was employed as key algorithm. Finally, a simulation and a lab-scale experiment were conducted to demonstrate the application of the proposed method.

Efficient Realization of Householder Transform Through Algorithm-Architecture Co-Design for Acceleration of QR Factorization

We present efficient realization of Householder Transform (HT) based QR factorization through algorithm-architecture co-design where we achieve performance improvement of 3-90x in-terms of Gflops/watt over state-of-the-art multicore, General Purpose Graphics Processing Units (GPGPUs), Field Programmable Gate Arrays (FPGAs), and ClearSpeed CSX700. Theoretical and experimental analysis of classical HT is performed for opportunities to exhibit higher degree of parallelism where parallelism is quantified as a number of parallel operations per level in the Directed Acyclic Graph (DAG) of the transform. Based on theoretical analysis of classical HT, an opportunity re-arrange computations in the classical HT is identified that results in Modified HT (MHT) where it is shown that MHT exhibits 1.33x times higher parallelism than classical HT. Experiments in off-the-shelf multicore and General Purpose Graphics Processing Units (GPGPUs) for HT and MHT suggest that MHT is capable of achieving slightly better or equal performance compared to classical HT based QR factorization realizations in the optimized software packages for Dense Linear Algebra (DLA). We implement MHT on a customized platform for Dense Linear Algebra (DLA) and show that MHT achieves 1.3x better performance than native implementation of classical HT on the same accelerator. For custom realization of HT and MHT based QR factorization, we also identify macro operations in the DAGs of HT and MHT that are realized on a Reconfigurable Data-path (RDP). We also observe that due to re-arrangement in the computations in MHT, custom realization of MHT is capable of achieving 12% better performance improvement over multicore and GPGPUs than the performance improvement reported by General Matrix Multiplication (GEMM) over highly tuned DLA software packages for multicore and GPGPUs which is counter-intuitive.

О выделении макроопераций из вычислительных процессов сортировки массивов данных

The paper dwells computation process (CP) array sorting from the simple to the efficient. Computation process present’s as a block flowchart for clarity. Each process are extracted macro operations (MO), which are functionality finished pieces of CP. Lists of MO for each sorting algorithm put it together and summarized – minimizing number of MO. This approach will allow careful consideration most CP as it applies to dedicated MO.

Evaluation of Sweet Sauce Characteristic Taste Based on Electronic Tongue Technology

A commercial electronic tongue (ET) with specific sensors was applied on taste distinction and physicochemical characterization of seven kinds of sweet sauces. The response signals of ET sensors were analyzed by Principal Component Analysis (PCA) and Discriminant Factor Analysis (DFA). Meanwhile, these signals were transformed into the four relative taste scores (sourness, saltiness, umami and sweetness) by macro operation, followed by comparing with the corresponding four physiochemical indexes (total acids, sodium chloride, amino nitrogen and reducing sugars) which were determined by the methods in GB/T. The results show that ET can be used to distinguish different kinds of sweet sauces according to overall taste. Moreover, the intensity order of taste scores that obtained from ET is basically matched with the sequence of the corresponding physicochemical indexes, which proves that ET technique can be an effective approach to monitor and guarantee the quality of sweet sauce on line.

Detecting Macro Viruses- A TRIZ Based Analysis

The macro viruses are easy to create but difficult to detect. Even for a virus scanner it is difficult to decide which macro is a virus and which macro is not, as a user macro may also create files, send emails and do all such activities that a macro virus can do. It is difficult to differentiate a genuine macro and a virus macro as both of them do similar type of jobs. Suspecting a macro to be virus just because it is 'writing to a file' may result in false positives. It is necessary to improve the emulation method, like statically analyzing macro operations within a document, to save system resources and detect macro viruses more effectively.Signature scanning can detect macro viruses. But this method is not very effective as there are plenty of new macro viruses created on every day whose signatures are yet to be updated in the signature database. Integrity checking does not work for detecting macro viruses, as comparing checksums is not possible for document files, which are usually modified by users on a regular basis. A heuristic scanning is most effective to detect macro viruses of all types, old and new. The heuristic technique does not require exact signatures of known viruses. It just examines a target program and analyzes its code to determine if the code appears virus-like.

Targeted Induction of Lung Endothelial Cell Apoptosis Causes Emphysema-like Changes in the Mouse

Pulmonary gas exchange relies on a rich capillary network, which, together with alveolar epithelial type I and II cells, form alveolar septa, the functional units in the lung. Alveolar capillary endothelial cells are critical in maintaining alveolar structure, because disruption of endothelial cell integrity underlies several lung diseases. Here we show that targeted ablation of lung capillary endothelial cells recapitulates the cellular events involved in cigarette smoke-induced emphysema, one of the most prevalent nonneoplastic lung diseases. Based on phage library screening on an immortalized lung endothelial cell line, we identified a lung endothelial cell-binding peptide, which preferentially homes to lung blood vessels. This peptide fused to a proapoptotic motif specifically induced programmed cell death of lung endothelial cells in vitro as well as targeted apoptosis of the lung microcirculation in vivo. As early as 4 days following peptide administration, mice developed air space enlargement associated with enhanced oxidative stress, influx of macrophages, and up-regulation of ceramide. Given that these are all critical elements of the corresponding human emphysema caused by cigarette smoke, these data provide evidence for a central role for the alveolar endothelial cells in the maintenance of lung structure and of endothelial cell apoptosis in the pathogenesis of emphysema-like changes. Thus, our data enable the generation of a convenient mouse model of human emphysema. Finally, combinatorial screenings on immortalized cells followed by in vivo targeting establishes an experimental framework for discovery and validation of additional ligand-directed pharmacodelivery systems.

Embedded DRAM design and architecture for the IBM 0.11-µm ASIC offering

This paper presents an overview of the macro design, architecture, and built-in self-test (BIST) implementation as part of the IBM third-generation embedded dynamic random-access memory (DRAM) for the IBM Blue Logic® 0.11-µm application-specific integrated circuit (ASIC) design system (CU-11). Issues associated with embedding DRAM in an ASIC design are identified and addressed, including fundamental DRAM core function, user interface, test, and diagnosis. Macro operation and organization are detailed and contrasted with traditional DRAM designs. The use of BIST, a key enabler for embedded DRAM, is discussed while highlighting innovations required by the embedded DRAM.

Hierarchical compilation of macro dataflow graphs for multiprocessors with local memory

This paper presents a hierarchical approach for compiling macro dataflow graphs for multiprocessors with local memory. Macro dataflow graphs comprise several nodes (or macro operations) that must be executed subject to prespecified precedence constraints. Programs consisting of multiple nested loops, where the precedence constraints between the loops are known, can be viewed as macro dataflow graphs. The hierarchical compilation approach comprises a processor allocation phase followed by a partitioning phase. In the processor allocation phase, using estimated speedup functions for the macro nodes, computationally efficient techniques establish the sequencing and parallelism of macro operations for close-to-optimal run-times. The second phase partitions the computations in each macro node to maximize communication locality for the level of parallelism determined by the processor allocation phase. The same approach can also be used for programs consisting of multiple loop nests, when each of the nested loops can be characterized by a speedup function. These ideas have been implemented in a prototype structure-driven compiler, SDC, for expressions of matrix operations. The paper presents the performance of the compiler for several matrix expressions on a simulator of the Alewife multiprocessor.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Adaptive and Evolutionary Robotics - A New Architecture for Learning-Based Autonomous Space Robot

A learning-type, hierarchical architecture for an autonomous robot management and control system is proposed. The upper task planner generates a sequence of robot movements which achieves the given objectives and the lower task executer realizes them in the robot movements using sensor feedbacks. At the executer level, some operational parameters, which dictate how the robot should move in response to the sensor information, are learned, which together with the sensor feed backs compensates for the gaps between the robotic world and the real world. At the task planning level, learning of macro operators is performed. The system as a whole provides the adaptability to the environmental changes or new situations, and also realizes the evolutional growth of thhe versatility. Preliminary laboratory experiments using two manipulators and some cameras for block construction tasks have been performed, which indicates that the proposed architecture is promising

Framework for query optimization in distributed statistical databases

Recently, there has been a growing interest in statistical database (SDB) research. When SDBs are dispersed among computing facilities at various sites (e.g., in health-care networks) an additional dimension is added to the already difficult problems faced by the SDB designer. A distributed statistical database management system (DS-DBMS) consists of micro data (i.e., raw data) and macro data (i.e., aggregated objects called summary tables), which can be considered essentially as aggregated views of the raw data in a special format. The first part of the paper gives an overview of a model for the representation of both raw data (micro data) and summary tables (macro data). The model is an extension of the relational model (so that existing distributed database systems can be exploited). Most of the first part is devoted to defining operations on macro data sets. Based on these operations, a set of equivalent relational operations is described, as one of the main objectives in defining the micro and macro data sets, and the operations on them, has been to use as much as possible the capabilities that are already offered by most relational DBMSs. The second part of the paper deals with one of the important aspects of performance in a DS-DBMS, namely, the efficient processing of queries. This is heavily influenced by the performance of query optimizers. However, to provide query optimization in a DS-DBMS, special issues are raised that manifest themselves in different scenarios. Some of the important issues and problems raised are discussed and solutions proposed. In addition, a set of transformations on macro operations (similar to those in relational algebra) are introduced, which can be used for optimizing queries in DS-DBMSs.

Maximum likelihood sequence estimators: a geometric view

Communication issues are described in terms of macro operations between the data and the observation spaces. The problem of recovering the data is related to the inversion of an operator (the channel mapping); for this reason results available in linear algebra and functional analysis are applicable. Traditional concepts in communications are identified with these operations. An approach to the maximum-likelihood sequence estimator based on a sufficient statistic derived from these concepts is proposed. Intersymbol interference is removed by linear equalization, and a Viterbi-like dynamic programming algorithm takes into account the correlated noise in the metric evaluation. The performance of suboptimal receivers obtained by means of metric simplification is analyzed. >