Best-known Methods Research Articles

Linear Algebra on Parallel Structures Using Wiedemann Algorithm to Solve Discrete Logarithm Problem

Discrete logarithm problem, popularly known as DLP has been the heart of many Public Key Infrastructures that are being used today. DLP belongs to the category of hard problems. Many ideas have been proposed to solve DLP. The best-known methods are Number Field Sieve (NFS) class algorithms. The Function Field Sieve (FFS) algorithm is one of the NFS class algorithms to solve DLP in an extension field, i.e. Galois field . This FFS involves three phases such as Function Sieving, Filtering, and Linear Algebra. Linear Algebra phase involves solving huge sparse matrices/system of linear equations. The most popular methods to solve sparse matrices are Lanczos and Wiedemann method. Recently, Wiedemann method got attention to solve the linear system of equations such as Ax = 0. This paper studies Wiedemann algorithm to solve based on the factors of ; since the DLP is defined over . The different cases considered in this paper are (1) is a prime of size less than n bits, (2) is a composite number with one factor of size more than n bits, and (3) is a composite number with more than one factors of n bits. The naive Wiedemann is considered in case 1, paralleled version of Joux and Pierrot [“Nearly sparse linear algebra and application to discrete logarithms computation,” preprint Contemporary Developments in Finite Fields and Applications, 2016.] is considered in case 2 and the client-server model along with the paralleled version of Wiedemann for case 3. Algorithms for all the three cases of solving are designed, analysed, experimented, and tested under the conditions based on density and size of matrices. The experiments are carried out on the matrices obtained from filtering step of FFS. The results are analysed and reported. From the results, it is shown that the communication cost between master and the slaves is to be considered and minimum density in the matrix is to be maintained in the previous step of linear algebra of FFS, i.e. filtering step to avoid trivial solutions. The configuration of the system used for experiments is 64 bit Intel (R) Xeon (R) CPU E5-2650 v3 @ 2.30 GHz with 40 cores and Intel(R) Core i7-6500U CPU @ 2.50 GHz.

HRCM: An Efficient Hybrid Referential Compression Method for Genomic Big Data.

With the maturity of genome sequencing technology, huge amounts of sequence reads as well as assembled genomes are generating. With the explosive growth of genomic data, the storage and transmission of genomic data are facing enormous challenges. FASTA, as one of the main storage formats for genome sequences, is widely used in the Gene Bank because it eases sequence analysis and gene research and is easy to be read. Many compression methods for FASTA genome sequences have been proposed, but they still have room for improvement. For example, the compression ratio and speed are not so high and robust enough, and memory consumption is not ideal, etc. Therefore, it is of great significance to improve the efficiency, robustness, and practicability of genomic data compression to reduce the storage and transmission cost of genomic data further and promote the research and development of genomic technology. In this manuscript, a hybrid referential compression method (HRCM) for FASTA genome sequences is proposed. HRCM is a lossless compression method able to compress single sequence as well as large collections of sequences. It is implemented through three stages: sequence information extraction, sequence information matching, and sequence information encoding. A large number of experiments fully evaluated the performance of HRCM. Experimental verification shows that HRCM is superior to the best-known methods in genome batch compression. Moreover, HRCM memory consumption is relatively low and can be deployed on standard PCs.

Proton Exchange Membrane Fuel Cell Stack Design Optimization Using an Improved Jaya Algorithm

Fuel cell stack configuration optimization is known to be a problem that, in addition to presenting engineering challenges, is computationally hard. This paper presents an improved computational heuristic for solving the problem. The problem addressed in this paper is one of constrained optimization, where the goal is to seek optimal (or near-optimal) values of (i) the number of proton exchange membrane fuel cells (PEMFCs) to be connected in series to form a group, (ii) the number of such groups to be connected in parallel, and (iii) the cell area, such that the PEMFC assembly delivers the rated voltage at the rated power while the cost of building the assembly is as low as possible. Simulation results show that the proposed method outperforms four of the best-known methods in the literature. The improvement in performance afforded by the proposed algorithm is validated with statistical tests of significance.

Dynamic Discretization Discovery for Solving the Time-Dependent Traveling Salesman Problem with Time Windows

We present a new solution approach for the time-dependent traveling salesman problem with time windows. This problem considers a salesman who departs from his home, has to visit a number of cities within a predetermined period of time, and then, returns home. The problem allows for travel times that can depend on the time of departure. We consider two objectives for the problem: (1) a makespan objective that seeks to return the salesman to his home as early as possible and (2) a duration objective that seeks to minimize the amount of time that he is away from his home. The solution approach is based on an integer programming formulation of the problem on a time-expanded network, because doing so enables time dependencies to be embedded in the definition of the network. However, because such a time-expanded network (and thus, the integer programming formulation) can rapidly become prohibitively large, the solution approach uses a dynamic discretization discovery framework, which has been effective in other contexts. Our computational results indicate that the solution approach outperforms the best-known methods on benchmark instances and is robust with respect to instance parameters.

Sample preparation method for visualization of nanoparticulate captured on mixed cellulose ester filter media by enhanced darkfield microscopy and hyperspectral imaging

A significant hurdle in conducting effective health and safety hazard analysis and risk assessment for the nanotechnology workforce is the lack of a rapid method for the direct visualization and analysis of filter media used to sample nanomaterials from work environments that represent potential worker exposure. Current best-known methods include transmission electron microscopy (TEM) coupled with energy dispersive x-ray spectroscopy (EDS) for elemental identification. TEM-EDS is considerably time-, cost-, and resource-intensive, which may prevent timely health and safety recommendations and corrective actions. A rapid screening method is currently being explored using enhanced darkfield microscopy with hyperspectral imaging (EDFM-HSI). For this approach to be effective, rapid, and easy, sample preparation that is amenable to the analytical technique is needed. Here, we compare the sample preparation steps for mixed cellulose ester (MCE) filter media specified in NIOSH Method 7400-Asbestos and Other Fibers by Phase Contrast Microscopy (PCM)-against a new method, which involves saturation of the filter media with acetone. NIOSH Method 7400 was chosen as a starting point since it is an established technique for preparing transparent MCE filters for optical microscopy. Limitations in this method led to the development and comparison of a new method. The new method was faster, easier, and rendered filters more transparent, resulting in improved visualization and analysis of nanomaterials via EDFM-HSI. This new method is suitable for a rapid screening protocol due to its speed, ease of use, and the improvement in image acquisition and analysis.

An Effective Minimal Probing Approach With Micro-Cluster for Distance-Based Outlier Detection in Data Streams

Outlier detection in data streams is considered a significant task in data mining that targets the discovery of elements in an unprecedented data arrival rate. The fast arrival of data demands fast computation within the shortest period, and with minimal memory usage. Detecting distance-based outliers in such a scenario are more complicated. Existing techniques such as the two best-known methods - Micro-Cluster Outlier Detection (MCOD) and Thresh_LEAP have presented some solutions to these challenges. However, the combination of the strength of both techniques can be a lot more improvement to the individual methods proposed. Therefore, in this paper, we propose a method called Micro-Cluster with Minimal Probing (MCMP), which is a hybrid approach of the combination of the strength of MCOD and Thresh_LEAP. We offer a new distance-based outlier detection technique to minimize the computational cost in detecting distance-based outliers effectively. The proposed MCMP technique is comprised of two approaches. Firstly, we adopt micro-clusters to mitigate the range query search. Then, to deal with the objects outside the micro-clusters, we propose the concept of differentiating between strong and trivial inliers. The proposed method improves the computational speed and memory consumption, while simultaneously maintaining the outlier detection accuracy. Our experiments are conducted on both real-world and synthetic data sets. We varied the window size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(w)$ </tex-math></inline-formula> , neighbor count threshold <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(k)$ </tex-math></inline-formula> and distance threshold <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(R)$ </tex-math></inline-formula> , and observed that our method outperforms the state-of-the-art methods in both CPU time and memory consumption in the majority of the datasets.

Improving Thai Word and Sentence Segmentation Using Linguistic Knowledge

Word boundary ambiguity in word segmentation has long been a fundamental challenge within Thai language processing. The Conditional Random Fields (CRF) model is among the best-known methods to have achieved remarkably accurate segmentation. Nevertheless, current advancements appear to have left the problem of compound words unaccounted for. Compound words lose their meaning or context once segmented. Hence, we introduce a dictionary-based word-merging algorithm, which merges all kinds of compound words. Our evaluation shows that the algorithm can accomplish a high-accuracy of word segmentation, with compound words being preserved. Moreover, it can also restore some incorrectly segmented words. Another problem involving a different word-chunking approach is sentence boundary ambiguity. In tackling the problem, utilizing the part of speech (POS) of a segmented word has been found previously to help boost the accuracy of CRF-based sentence segmentation. However, not all segmented words can be tagged. Thus, we propose a POS-based word-splitting algorithm, which splits words in order to increase POS tags. We found that with more identifiable POS tags, the CRF model performs better in segmenting sentences. To demonstrate the contributions of both methods, we experimented with three of their applications. With the word merging algorithm, we found that intact compound words in the product of topic extraction can help to preserve their intended meanings, offering more precise information for human interpretation. The algorithm, together with the POS-based word-splitting algorithm, can also be used to amend word-level Thai-English translations. In addition, the word-splitting algorithm improves sentence segmentation, thus enhancing text summarization.

Critical Literacy and the Communicative Approach: Gaps and Intersections

ABSTRACT The objective of this paper is to foster reflections on the similarities and differences between the Communicative Approach, one of the best-known methods for teaching English as a Foreign Language (EFL) around the world, and Critical Literacy, a relatively recent trend in the Brazilian EFL scenario, as suggested by the National Guidelines for High School Teaching (BRASIL, 2006). First, we will address the theoretical traditions that give rise to the two teaching approaches, explaining their main concepts. We will then discuss the similarities between the two approaches, as well as their main differences. We claim that the two teaching approaches, however different in their epistemological underpinnings, are not incompatible, but rather complementary. Finally, we suggest an example of an integrated activity that aims to reconcile the objectives of the two approaches.

High-performance architecture for digital transform processing

The digital transforms are intensive in multiplication and accumulation operations which have a high computational cost. Advances in computer arithmetic and digital technologies allow simplifying the processing of complex algorithms when they are implemented in modern circuits. New computation techniques can be explored to provide efficient operational methods for implementing algorithms that avoid much of the complex and costly mathematical operations. This work aims to design a high-performance architecture for computing some common digital transforms. The proposed architecture has been compared to other methods. The transform used as example in this work is the discrete cosine transform. The results show that the proposal offers high-performance results comparable or better than best-known methods.

An Improved Firefly Algorithm for the Unrelated Parallel Machines Scheduling Problem With Sequence-Dependent Setup Times

Research in the area of unrelated parallel machine scheduling problem (UPMSP) with sequence-dependent setup times has received little attention from the research community. However, this problem is NP-hard even without considering the setup times, and when sequence-dependent setup times are included, finding optimal solutions becomes very difficult, especially for the problems with large dimension. In this paper, a firefly algorithm (FA) which is refined with a local search solution improvement mechanism is presented to solve this problem, with the objective of reaching a near-optimum solution. Since the classical FA was originally designed for continuous optimization problems, a new solution representation scheme is designed to make the FA suitable for a combinatorial optimization problem such as the UPMSP. Three different popular metaheuristic algorithms are developed in parallel to verify and measure the effectiveness of the proposed algorithm. More so, the success of the novel firefly scheduling method is measured by comparing the quality of its solutions against the best-known methods from the literature. An exhaustive computational and statistical analysis is carried out to show an excellent performance of the new method on a large set of problem instances. The numerical results show that the improved FA is competitive, fast, and efficient and provide good quality solutions for both small and large problem instances.

DETERMINATION OF STRATEGIC DEVELOPMENT DIRECTIONS OF RESTAURANT INDUSTRY INSTITUTIONS ON BENCHMARKING BASES

Introduction. The article deals with the issues of strategic development of restaurant industry institutions. Nowadays, the fractal structuring is one of the most efficient tools for the study of complex organizations. This structuring has allowed using a hierarchical system of indicators to carry out a strategic assessment and develop the direction of strategic development of enterprises. Purpose. The article aims is to determine the directions of strategic development of the restaurants of Kharkiv, which are the part of the networks, on the basis of benchmarking. For this purpose, the system of restaurant management is proposed to be structured according to a fractal hierarchy using evolutionary principles. Method. The method of taxonomic analysis and modern methods of benchmarking have been used to find and study the best-known methods of generating business and helping to improve service delivery processes relatively quickly and at the lowest cost. These methods aim at assessing and determining the directions of strategic development of restaurant facilities in the networks. Results. Based on the data as for the activity of restaurants in the network structure and the results of the assessment of their strategic development, the reduction of the distance from the leading enterprise by the level of the taxonomic indicator has been calculated by improving each factor. The factors can determine the level of strategic development in the enterprise under the analyses. The calculations have been executed according to the indicators, which reflect the fractal structure of the organization of the restaurant industry. The results of the analysis have allowed the identification of such strategic directions as menu optimization, improvement of the quality of service, net profit, turnover, establishment of rates and efficiency of using the network benefits (profitability).

Ancestral Sequence Reconstruction with Maximum Parsimony.

One of the main aims in phylogenetics is the estimation of ancestral sequences based on present-day data like, for instance, DNA alignments. One way to estimate the data of the last common ancestor of a given set of species is to first reconstruct a phylogenetic tree with some tree inference method and then to use some method of ancestral state inference based on that tree. One of the best-known methods both for tree inference and for ancestral sequence inference is Maximum Parsimony (MP). In this manuscript, we focus on this method and on ancestral state inference for fully bifurcating trees. In particular, we investigate a conjecture published by Charleston and Steel in 1995 concerning the number of species which need to have a particular state, say a, at a particular site in order for MP to unambiguously return a as an estimate for the state of the last common ancestor. We prove the conjecture for all even numbers of character states, which is the most relevant case in biology. We also show that the conjecture does not hold in general for odd numbers of character states, but also present some positive results for this case.

Analysis of elevation interpolation methods for creating digital elevation models

Interpolation methods for creating digital elevation models using geoinformation system data are considered. For this purpose, the best-known methods are analyzed: inverse distance weighting (IDW), the Krige (or kriging) method, ANUDEM, spline interpolation, the natural neighbor method, and the method based on the construction of a triangular irregular network (TIN) model). The modeling accuracy is estimated for the area located between the Ui, Tara, and Irtysh Rivers in the Omsk region of West Siberia (Russia). Analysis of the results of estimating the accuracy of a digital relief model created using the ArcGIS 10 geoinformation system shows that the best results are obtained using spline methods and inverse distance weighting.

Scaling statistical multiple sequence alignment to large datasets

BackgroundMultiple sequence alignment is an important task in bioinformatics, and alignments of large datasets containing hundreds or thousands of sequences are increasingly of interest. While many alignment methods exist, the most accurate alignments are likely to be based on stochastic models where sequences evolve down a tree with substitutions, insertions, and deletions. While some methods have been developed to estimate alignments under these stochastic models, only the Bayesian method BAli-Phy has been able to run on even moderately large datasets, containing 100 or so sequences. A technique to extend BAli-Phy to enable alignments of thousands of sequences could potentially improve alignment and phylogenetic tree accuracy on large-scale data beyond the best-known methods today.ResultsWe use simulated data with up to 10,000 sequences representing a variety of model conditions, including some that are significantly divergent from the statistical models used in BAli-Phy and elsewhere. We give a method for incorporating BAli-Phy into PASTA and UPP, two strategies for enabling alignment methods to scale to large datasets, and give alignment and tree accuracy results measured against the ground truth from simulations. Comparable results are also given for other methods capable of aligning this many sequences.ConclusionsExtensions of BAli-Phy using PASTA and UPP produce significantly more accurate alignments and phylogenetic trees than the current leading methods.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3101-8) contains supplementary material, which is available to authorized users.

Biotechnological production of γ-decalactone, a peach like aroma, by Yarrowia lipolytica.

The request for new flavourings increases every year. Consumer perception that everything natural is better is causing an increase demand for natural aroma additives. Biotechnology has become a way to get natural products. γ-Decalactone is a peach-like aroma widely used in dairy products, beverages and others food industries. In more recent years, more and more studies and industrial processes were endorsed to cost-effect this compound production. One of the best-known methods to produce γ-decalactone is from ricinoleic acid catalyzed by Yarrowia lipolytica, a generally regarded as safe status yeast. As yet, several factors affecting γ-decalactone production remain to be fully understood and optimized. In this review, we focus on the aromatic compound γ-decalactone and its production by Y. lipolytica. The metabolic pathway of lactone production and degradation are addressed. Critical analysis of novel strategies of bioprocess engineering, metabolic and genetic engineering and other strategies for the enhancement of the aroma productivity are presented.

Status and Prospects of Deformational Methods For Refining the Microstructure of Bulk Materials

The current interest in obtaining metals and alloys with an ultrafine-grained structure is due to the qualitatively new level of their mechanical properties. Since an ultrafine-grained structure is formed as a result of high-rate plastic deformation (HPD), this article presents several alternative definitions of HPD. Continuous HPD methods are the methods that are of practical interest. However, several unresolved scientific-technical, technological, and organizational problems are making it necessary to resort to fundamentally new approaches to developing deformation-based methods of refining microstructural components. This article presents examples of the best-known continuous deformational methods of refining the structural components of metals and alloys. On the other hand, it is also necessary to consider that certain bulk metals and alloys with an ultrafine-grained structure have properties that limit or prevent their practical use. The results obtained from an analysis are used to determine the main avenues being pursued in the development of deformational methods of refining structural components. It is particularly important to consider the use of mini-mills, which can create technological systems based on integrated and consolidated components of modular equipment.

Finding first order differential invariants through the [formula omitted]-function

Finding first order differential invariants through the [formula omitted]-function

Host-Guest Chemistry between Perylene Diimide (PDI) Derivatives and 18-Crown-6: Enhancement in Luminescence Quantum Yield and Electrical Conductivity.

Perylene diimide (PDI) derivatives exhibit a high propensity for aggregation, which causes the aggregation-induced quenching of emission from the system. Host-guest chemistry is one of the best-known methods for preventing aggregation through the encapsulation of guest molecules. Herein we report the use of 18-crown-6 (18-C-6) as a host system to disaggregate suitably substituted PDI derivatives in methanol. 18-C-6 formed complexes with amino-substituted PDIs in methanol, which led to disaggregation and enhanced emission from the systems. Furthermore, the embedding of the PDI⋅18-C-6 complexes in poly(vinyl alcohol) (PVA) films generated remarkably high emission quantum yields (60-70 %) from the PDI derivatives. More importantly, the host-guest systems were tested for their ability to conduct electricity in PVA films. The electrical conductivities of the self-assembled systems in PVA were measured by electrochemical impedance spectroscopy (EIS) and the highest conductivity observed was 2.42×10(-5) S cm(-1) .

Clocked lambda calculus

One of the best-known methods for discriminating λ-terms with respect to β-convertibility is due to Corrado Böhm. The idea is to compute the infinitary normal form of a λ-term M, the Böhm Tree (BT) of M. If λ-terms M, N have distinct BTs, then M ≠βN, that is, M and N are not β-convertible. But what if their BTs coincide? For example, all fixed point combinators (FPCs) have the same BT, namely λx.x(x(x(. . .))).We introduce a clocked λ-calculus, an extension of the classical λ-calculus with a unary symbol τ used to witness the β-steps needed in the normalization to the BT. This extension is infinitary strongly normalizing, infinitary confluent and the unique infinitary normal forms constitute enriched BTs, which we call clocked BTs. These are suitable for discriminating a rich class of λ-terms having the same BTs, including the well-known sequence of Böhm's FPCs.We further increase the discrimination power in two directions. First, by a refinement of the calculus: the atomic clocked λ-calculus, where we employ symbols τp that also witness the (relative) positions p of the β-steps. Second, by employing a localized version of the (atomic) clocked BTs that has even more discriminating power.

How should we measure chunks? a continuing issue in chunking research and a way forward.

Generally defined, chunking is a process through which one reorganizes or groups presented information to compress information; it is one of the best-known methods of increasing the amount of information stored in memory. Chunking can occur by two different means: either through strategic reorganization based on familiarity or prior knowledge, or through grouping based on perceptual characteristics. An example of the former is using knowledge of acronyms to break a string of letters (e.g., AWOLNASAMIA) into smaller, separate groups (i.e., AWOL, NASA, MIA). In the case of the latter, more common with visual stimuli, one can form groups on the basis of similarity or proximity. Although both methods are considered part of the general phenomenon of chunking, it is the goal-directed, strategic chunking that is the focus of this piece. Although the process of chunking has been discussed as a mnemonic strategy in William James's Principles of Psychology (1890), it is most widely known through George Miller's paper, “The Magical Number Seven, Plus or Minus Two.” Miller (1956) primarily reviewed several studies that examined capacity limits in immediate recall; across various types of stimuli, a consistent recall limit of between five and nine items was observed. As a secondary emphasis, Miller also discussed recoding and subsequent implications on estimates of immediate memory capacity. Miller observed that if information was recoded into meaningful units (called chunks), this increased the amount of information that could be recalled, and thereby increased immediate memory span. This occurs because increased meaning through chunking or recoding increases the size of each respective chunk (e.g., Tulving and Patkau, 1962; Chase and Simon, 1973; Simon, 1974), but the number of chunks that can be stored in short-term memory remains constant, typically limited to four or fewer items (e.g., Cowan, 2001; Gobet and Clarkson, 2004; Mathy and Feldman, 2012). Despite the fact that Miller published his paper nearly 60 years prior, our understanding of chunking remains incomplete. In particular, though many chunking papers use a variety of methods to measure how chunks are formed and retrieved, it is unclear whether the majority of these methods of measuring chunks accurately reflect the internal cognitive processes that are involved in chunk formation. Before discussing this problem in further detail, I will briefly review well-known methods of measuring chunks and how these methods have been used in previous research (see Gilchrist and Cowan, 2012, for a detailed discussion of chunking and these measurement methods). For present purposes, I will be restricting these measurement methods to those involved in deliberate and goal-directed chunking of verbal materials, as these typically require more effortful processing.