Academic literature on the topic 'Genericity and performance'

A number of recent studies have argued that bilingual children's language comprehension and production may be affected by cross-linguistic influence. The overall aim of this study was to investigate whether the ability to judge the grammaticality of a construction in one language is affected by knowledge of the corresponding construction in the other language. We investigated how English–Italian and Spanish–Italian bilingual children and monolingual peers judged the grammaticality of plural NPs in specific and generic contexts in English and in Italian. We also explored whether language of the community, age, and the typological relatedness of the bilinguals’ two languages significantly affected their performance. While performance in English was overall poor, no significant differences existed between the English–Italian bilinguals and the monolinguals. In contrast, we found that knowledge of English affected the bilinguals’ ability to discriminate between grammatical and ungrammatical sentences in Italian. The English–Italian bilinguals were significantly less accurate than both the monolinguals and the Spanish–Italian bilinguals in a task where they simply had to rely on the local definite article cue to reject ungrammatical bare plurals in generic contexts. Language of the community and age also played a significant role in children's accuracy.

Usage control policies specify restrictions on the handling of data after access has been granted. The authors present the design and implementation of a framework for enforcing usage control requirements and demonstrate its genericity by instantiating it to two different levels of abstraction, those of the operating system and an enterprise service bus. This framework consists of a policy language, an automatic conversion of policies into enforcement mechanisms, and technology implemented on the grounds of trusted computing technology that makes it possible to detect tampering with the infrastructure. The authors show how this framework can, among other things, be used to enforce separation-of-duty policies. The authors provide a performance analysis.

Timeliness of the topic is conditioned by the need to keep up ample of static and transient stability margins in modern electrical power systems. The article object is to give a determination of present methods of evaluating damping of systems with synchronous machines, in the context of their effectiveness. And also to estimate the effectiveness of these methods in the performance of the task concerned with selecting best settings of automatic excitation regulators of synchronous generators. This analysis led us to the conclusions, in particular, about possibility to apply the root locus methods in estimating stability of electrical power systems, and also about genericity of the matrix method with the use of QR-algorithm, which is widely used in practice of calculating stability

SAC is a purely functional array processing language designed with numerical applications in mind. It supports generic, high-level program specifications in the style of APL. However, rather than providing a fixed set of built-in array operations, SAC provides means to specify such operations in the language itself in a way that still allows their application to arrays of any rank and size. This paper illustrates the major steps in compiling generic, rank- and shape-invariant SAC specifications into efficiently executable multithreaded code for parallel execution on shared memory multiprocessors. The effectiveness of the compilation techniques is demonstrated by means of a small case study on the PDE1 benchmark, which implements 3-dimensional red/black successive over-relaxation. Comparisons with HPF and ZPL show that despite the genericity of code, SAC achieves highly competitive runtime performance characteristics.

For more than 30 decades, data warehouses have been considered the only business intelligence storage system for enterprises. However, with the advent of big data, they have been modernized to support the variety and dynamics of data by adopting the data lake as a centralized data source for heterogeneous sources. Indeed, the data lake is characterized by its flexibility and performance when storing and analyzing data. However, the absence of schema on the data during ingestion increases the risk of the transformation of the data lake into a data swamp, so the use of metadata management is essential to exploit the data lake. In this paper, we will present a conceptual metadata management model for the data lake. Our solution will be based on a functional architecture of the data lake as well as on a set of features allowing the genericity of the metadata model. Furthermore, we will present a set of transformation rules, allowing us to translate our conceptual model into an owl ontology.

Abstract Predicting tree frost tolerance is critical to select adapted species according to both the current and predicted future climate. The relative change in water to carbohydrate ratio is a relevant trait to predict frost acclimation in branches from many tree species. The objective of this study is to demonstrate the interspecific genericity of this approach across nine tree species. In the studied angiosperm species, frost hardiness dynamics were best correlated to a decrease in water content at the early stage of acclimation (summer and early autumn). Subsequently, frost hardiness dynamics were more tightly correlated to soluble carbohydrate contents until spring growth resumption. Based on different model formalisms, we predicted frost hardiness at different clade levels (angiosperms, family, genus and species) with high to moderate accuracy (1.5–6.0 °C root mean squared error (RMSE)) and robustness (2.8–6.1 °C prediction RMSE). The TOT model, taking all soluble carbohydrate and polyols into account, was more effective and adapted for large scale studies aiming to explore frost hardiness across a wide range of species. The ISC model taking the individual contribution of each soluble carbohydrate molecule into account was more efficient at finer scale such as family or species. The ISC model performance also suggests that the role of solutes cannot be reduced to a ‘bulk’ osmotic effect as could be computed if all of them were located in a single, common, compartment. This study provides sets of parameters to predict frost hardiness in a wide range of species, and clues for targeting specific carbohydrate molecules to improve frost hardiness.

AC regenerative electric drives (AC REDs) are widely used in metallurgical rolling due to their reliability, efficiency, and power sufficient to maintain the process. This paper reviews the latest achievements in building the grid connection circuits for the main AC REDs of rolling mills. The paper discusses multipulse connection circuits formed by various transformer types and algorithms for preprogrammed pulse-width modulation with selective harmonic elimination technique (PPWM with SHE) of three-level active front ends (AFE), provides the theoretical and practical measurement results, and gives recommendations for improving existing systems. For 6-, 12-, and 18-pulse grid connection circuits, switching patterns of AFE semiconductor modules with a smooth downward trend within the modulation index range from 0.7 to 1.15 are provided. A simulation was performed under comparable conditions on simulation models in the Matlab/Simulink to objectively evaluate the performance and opportunities of 6-, 12-, and 18-pulse grid connection circuits, including the three-level AFE and transformer specifications. The waveforms and spectra of the grid currents and transformer secondary winding phase currents are shown; total harmonic distortion (THD) factors have been calculated up to the 60th harmonic for various PPWM with SHE patterns. The results of simulation and experimental measurement on operating equipment have been compared. The paper is expected to provide a broad overview of multipulse connection circuits of the rolling mill’s main AC REDs, in particular, identify the latest solutions capable of significantly improving their electromagnetic compatibility with the grid. The results obtained are of high genericity and can be used by researchers and engineers to provide the electromagnetic compatibility of non-linear consumers in similar circuits, as well as design them.

Purpose Sustainability is an accepted measure of business performance, with reductions in energy demand a commonly practised sustainability initiative by multinational corporations (MNCs). Traditional energy models have limited scope when applied to the entire MNC as the models exhibit high data and time intensity, high technical proficiency, specificity of application and omission of non-manufacturing activities. The purpose of this paper is to propose a process centric energy model (PCEM), which adopts a novel approach of applying business processes for business energy assessment and optimisation. Business processes are a fundamental requirement of MNCs across all sectors. The defining features of the proposed model are genericity, reproducibility, minimum user input data, reduced modelling time and energy evaluation of non-manufacturing activities. The approach forwards the adoption of Industry 4.0, a subset of which focuses on business process automation or part thereof. Design/methodology/approach A quantitative approach is applied in development of the PCEM. The methodology is demonstrated by application to the procure to pay and electroplating business processes. Findings The PCEM quantifies and optimises the business energy demand and associated carbon dioxide emissions of the procure to pay and electroplating business processes, validating the application of business processes. The application demonstrates minimum user inputs as only equipment operational parameters are required and minimum modelling time as business process models and optimisation options are pre-defined requiring only user modification. As MNCs have common business processes across multiple sites, once a business process energy demand is quantified, its inputs are applied as the default in the proceeding sites, only requiring updating. The model has no specialist skills requirement enabling business wide use and eliminating costs associated with training and expert’s services. The business processes applied in the evaluation are developed by the researchers and are not as comprehensive as those in actual MNCs, but is sufficiently detailed to accurately calculate an MNC energy demand. The model databases are not exhaustive of all resources found in MNCs. Originality/value This paper provides a new approach to MNC business energy assessment and optimisation. The model can be applied to MNEs across all sectors. The model allows the integration of manufacturing and non-manufacturing activities, as it occurs in practice, providing holistic business energy assessment and optimisation. The model analyses the impacts of the adoption of Industry 4.0 technologies on business energy demand, CO2 emission and personnel hours.

Background The huge amount of clinical, administrative, and demographic data recorded and maintained by hospitals can be consistently aggregated into health data warehouses with a uniform data model. In 2017, Rouen University Hospital (RUH) initiated the design of a semantic health data warehouse enabling both semantic description and retrieval of health information. Objective This study aimed to present a proof of concept of this semantic health data warehouse, based on the data of 250,000 patients from RUH, and to assess its ability to assist health professionals in prescreening eligible patients in a clinical trials context. Methods The semantic health data warehouse relies on 3 distinct semantic layers: (1) a terminology and ontology portal, (2) a semantic annotator, and (3) a semantic search engine and NoSQL (not only structured query language) layer to enhance data access performances. The system adopts an entity-centered vision that provides generic search capabilities able to express data requirements in terms of the whole set of interconnected conceptual entities that compose health information. Results We assessed the ability of the system to assist the search for 95 inclusion and exclusion criteria originating from 5 randomly chosen clinical trials from RUH. The system succeeded in fully automating 39% (29/74) of the criteria and was efficiently used as a prescreening tool for 73% (54/74) of them. Furthermore, the targeted sources of information and the search engine–related or data-related limitations that could explain the results for each criterion were also observed. Conclusions The entity-centered vision contrasts with the usual patient-centered vision adopted by existing systems. It enables more genericity in the information retrieval process. It also allows to fully exploit the semantic description of health information. Despite their semantic annotation, searching within clinical narratives remained the major challenge of the system. A finer annotation of the clinical texts and the addition of specific functionalities would significantly improve the results. The semantic aspect of the system combined with its generic entity-centered vision enables the processing of a large range of clinical questions. However, an important part of health information remains in clinical narratives, and we are currently investigating novel approaches (deep learning) to enhance the semantic annotation of those unstructured data.

AbstractData science is a powerful field for gaining insights, comparing, and predicting behaviors from datasets. However, the diversity of methods and hypotheses needed to abstract a dataset exhibits a lack of genericity. Moreover, the shape of a dataset, which structures its contained information and uncertainties, is rarely considered. Inspired by state-of-the-art manifold learning and hull estimations algorithms, we propose a novel framework, the datascape, that leverages topology and graph theory to abstract heterogeneous datasets. Built upon the combination of a nearest neighbor graph, a set of convex hulls, and a metric distance that respects the shape of the data, the datascape allows exploration of the dataset’s underlying space. We show that the datascape can uncover underlying functions from simulated datasets, build predictive algorithms with performance close to state-of-the-art algorithms, and reveal insightful geodesic paths between points. It demonstrates versatility through ecological, medical, and simulated data use cases.

C++ est un langage de programmation multi-paradigme qui permet au développeur initié de mettre au point des algorithmes de traitement d'images. La force de langage se base sur plusieurs aspects. C++ est haut-niveau, cela signifie qu'il est possible de développer des abstractions puissantes mélangeant plusieurs styles de programmation pour faciliter le développement. En même temps, C++ reste bas-niveau et peut pleinement tirer partie du matériel pour fournir un maximum de performances. Il est aussi portable et très compatible ce qui lui permet de se brancher à d'autres langages de haut niveau pour le prototypage rapide tel que Python ou Matlab. Un des aspects les plus fondamentaux où le C++ brille, c'est la programmation générique. La programmation générique rend possible le développement et la réutilisation de briques logiciel comme des objets (images) de différentes natures (types) sans avoir de perte au niveau performance. Néanmoins, il n'est pas trivial de concilier les aspects de généricité, de performance et de simplicité d'utilisation. Le C++ moderne (post-2011) amène de nouvelles fonctionnalités qui le rendent plus simple et plus puissant. Dans cette thèse, nous explorons en premier un aspect particulier du C++20 : les concepts, dans le but de construire une taxonomie des types relatifs au traitement d'images. Deuxièmement, nous explorons une autre fonctionnalité ajoutée au C++20 : les ranges (et les vues). Nous appliquons ce design aux algorithmes de traitement d'images et aux types d'image, dans le but résoudre les problèmes liés, notamment, à la difficulté qu'il existe pour customiser les algorithmes de traitement d'image. Enfin, nous explorons les possibilités concernant la façon dont il est possible de construire un pont entre du code C++ générique statique (compile-time) et du code Python dynamique (runtime). Nous fournissons une solution hybride et nous mesurons ses performances. Nous discutons aussi les pistes qui peuvent être explorées dans le futur, notamment celles qui concernent les technologies JIT. Étant donné ces trois axes,nous voulons résoudre le problème concernant la conciliation des aspects de généricité, de performance et de simplicité d'utilisation
C++ is a multi-paradigm language that enables the initiated programmer to set up efficient image processing algorithms. This language strength comes from several aspects. C++ is high-level, which enables developing powerful abstractions and mixing different programming styles to ease the development. At the same time, C++ is low-level and can fully take advantage of the hardware to deliver the best performance. It is also very portable and highly compatible which allows algorithms to be called from high-level, fast-prototyping languages such as Python or Matlab. One of the most fundamental aspects where C++ really shines is generic programming. Generic programming makes it possible to develop and reuse bricks of software on objects (images) of different natures (types) without performance loss. Nevertheless,conciliating the aspects of genericity, efficiency, and simplicity is not trivial. Modern C++ (post-2011) has brought new features that made the language simpler and more powerful. In this thesis, we first explore one particular C++20aspect: the concepts, in order to build a concrete taxonomy of image related types and algorithms. Second, we explore another addition to C++20, ranges (and views), and we apply this design to image processing algorithms and image types in order to solve issues such as how hard it is to customize/tweak image processing algorithms. Finally, we explore possibilities regarding how we can offer a bridge between static (compile-time) generic C++ code and dynamic (runtime) Python code. We offer our own hybrid solution and benchmark its performance as well as discuss what can be done in the future with JIT technologies. Considering those three axes, we will address the issue regarding the way to conciliate generic programming, efficiency and ease of use

AbstractWe describe our implementation of downset-manipulating algorithms used to solve the realizability problem for linear temporal logic (LTL). These algorithms were introduced by Filiot et al. in the 2010s and implemented in the tools Acacia and Acacia+ in C and Python. We identify degrees of freedom in the original algorithms and provide a complete rewriting of Acacia in C++20 articulated around genericity and leveraging modern techniques for better performance. These techniques include compile-time specialization of the algorithms, the use of SIMD registers to store vectors, and several preprocessing steps, some relying on efficient Binary Decision Diagram (BDD) libraries. We also explore different data structures to store downsets. The resulting tool is competitive against comparable modern tools.

Abstract Accurate flame tracking plays a vital role in predicting the combustion characteristics of a system. This is even more critical for systems that evolve over time. Predicting relight performance of an aero combustor, predicting flame propagation due to gas leakage from a storage tank or during the thermal runaway of batteries, are some examples of such dynamic systems. Predicting accurate flame position also plays an important role in deriving the correct pollutant formation rate from a combustion system. The challenge with flame tracking through a 3D computational fluid dynamics (CFD) simulation comes from the requirement to have a good resolution of gradients along the flame front. This requirement can push the overall mesh count of any industrial cases to a very large value (several million-mesh count). Further, the global drive towards using hydrogen or hydrogen blended fuels for different combustion applications pushes the limits on having even finer cells since hydrogen is a fast-burning fuel and has a much thinner flame front compared to hydrocarbons. Solution-based mesh adaption approaches have been widely studied and tested by different research groups to generate the required finer meshes in the critical regions on the fly while keeping the overall mesh count to a manageable level. However, these approaches are typically applicable for a set of problems, and therefore, there is a need for a generic approach suitable for a broader range of problems. This work explores various parameters and specific weightage factors to predict correct flame-tracking outcomes for different types of flames. The selections of flow quantities (flow-variables, their gradients, curvatures) are performed using simple flames and flow configurations. The functions based on selected flow-quantities derived from these studies are then tested to predict the results for the more complex set of published flames like the Engine Combustion Network (ECN) spray flame and Knowledge for Ignition, Acoustics and Instabilities (KIAI) five-burner configuration (liquid and gas fuel). Derived adaption criteria are found to predict the correct flame tracking behavior in terms of transient evolution of flame front, flame propagation, and ignition timing of burners. The parameters used for the study are identified keeping genericity as the key point, and thus making sure that the derived adaption functions can be applied across different types of fuel blends, combustion systems (gaseous or liquid fuel-based systems) and combustion models, for example species transport or mixture fraction-based models.