Miracle-dz/UniFAR-FTUs huggingface dataset:
Task-based incidental vocabulary learning in L 2 Arabic : The role of proficiency and task performance

This study tests the claim that word learning in a second language are contingent upon a task’s involvement load (i.e. the amount of need, search, and evaluation it imposes), as proposed by Laufer and Hulstijn (2001). Fifty-three English-speaking learners of Arabic were assigned to one of three vocabulary learning tasks that varied in the degree of involvement: reading comprehension with glosses (low), fill-in-the-gap task (medium), and sentence writing (high). Ten words, selected based on a pretest, were targeted in the tasks. Results showed a main effect of task, with the sentence writing task yielding the highest rates of vocabulary learning, followed by the gap-fill task, and finally the reading comprehension task. A significant correlation was found between accuracy of performance across participants and their subsequent vocabulary acquisition in the immediate posttest. Within groups, only the performance of the writing group correlated significantly with their posttest scores. Results of the present study validate the hypothesis and point to multiple factors at play in incidental vocabulary acquisition. The study provides further arguments to refine the hypothesis and implement pedagogical practices that accommodate incidental learning in foreign language settings.
Phonological Adaptation and Morphosyntactic Integration in Igbo-English Insertional

In the insertional pattern of codeswitching described in a number of EuropeanAfrican language pairs, the African language is the matrix language that provides the morphosyntactic frame into which elements of the (embedded) European language are inserted. Morphosyntactic integration, especially of inserted verbs and verbal expressions, is a defining feature of such codeswitching. The study describes insertional codeswitching in which Igbo, a West-Congo language, is the matrix language, and English, the embedded language. It notes two major phonological differences (segmental and suprasegmental) between Igbo and English, namely, a harmony vs. nonharmony vowel system and a tone vs. stress system. As a result of such disparity, English verbs often lack the requisite phonological features for morphosyntactic integration into Igbo. This necessitates the phonological adaptation of English vowel segments to fit into the Igbo vowel system, and of English syllable stress to fit into the Igbo tonal system. English verbs with phonologically adapted features are thus enabled to achieve full morphosyntactic integration into Igbo and to function like Igbo verbs in terms of ability to select inflectional affixes and subject pronouns on the basis of tone and vowel harmony. Phonological adaptation is therefore observed to precede and to form the basis of the morphosyntactic integration of embedded language (English) verbs inserted into a matrix language (Igbo) frame. The patterns of phonological adaptation necessary for the morphosyntactic integration of English verbs into Igbo are described in the study and it is concluded that Igbo-English insertional codeswitching is a highly rule-governed linguistic phenomenon.
Relationships between Hydrotime Parameters and Seed Vigor in Sugar Beet

The hydrotime concept is a unifying model to describe the patterns of germination that occur in response to the water potential ( ) of a seed’s environment. The question is, is there a relationship between the physiological status (i.e., hydrotime parameters) and early vigor? Four replicates were conducted, each consisting of 50 or 25 seeds (in a germination test and seedling growth, respectively) in which five cultivars (‘IC’, ‘H5505’, ‘PP22’, ‘PP8’, ‘7233’ and ‘BR1’) of sugar beet seeds (Beta vulgaris L.) were germinated at five water potentials. Germination rate and final germination percentage were severely affected by reduced water potential in all cultivars. The predicted germination time courses at the various and temperature values generally fitted well with the observed germination data by an R value of 0.99 in all cultivars. The b(50) varied between -1.30 and -1.67 MPa. HT(50) reduced from about 85 MPad to 100 MPad. There were significant differences among the six cultivars from five water potentials for germination rate and percentage, seedling dry weight and seedling length. Results indicated a correlation between germination traits and hydrotime model parameters. There was a significant relationship between base water potential and germination percentage (r = 90*) and rate (r = 0.81*), seedling dry weight (r = 0.89*) and seedling length (r = 0.85*). However, there was a non-significant relationship between hydrotime value and seed vigor parameters. Here, lower base water potential also contributed to high early vigor. Thus, plant breeding efforts or physiological remedies should be focused on improvement of cultivars with a lower base water potential. _____________________________________________________________________________________________________________
emporal variation in development of ecosystem services from oyster eef restoration egan

Restoration ecology relies heavily on ecosystem development theories that generally assume development of fully functioning natural systems over time, but often fail to identify the time-frame required for provision of desired functions, or acknowledge different pathways of functional development. In estuaries, a decline of overall habitat quality and functioning has led to significant efforts to restore critical ecosystem services, recently through the creation and restoration of oyster reefs. Oyster reef restoration generally occurs with goals of (1) increasing water quality via filtration through sustainable oyster recruitment, (2) stabilizing shorelines, and (3) creating and enhancing critical estuarine habitat for fish and invertebrates. We restored over 260 m2 of oyster reef habitat in coastal Louisiana and followed the development and provision of these ecosystem services from 2009 through 2012. Oysters recruited to reefs immediately, with densities of oysters greater than 75 mm exceeding 80 ind m−2 after 3 years, and provision of filtration rates of 1002 ± 187 L h−1 m−2; shoreline stabilization effects of the created reefs were minimal over the three years of monitoring, with some evidence of positive shoreline stabilization during higher wind/energy events only; increased nekton abundance of resident, but not larger transient fish was immediately measurable at the reefs, however, this failed to increase through time. Our results provide critical insights into the development trajectories of ecosystem services provided by restored oyster reefs, as well as the mechanisms mediating these changes. This is critical both ecologically to understand how and where a reef thrives, and for policy and management to guide decision-making
Dynamic clustering and compressive data gathering algorithm for energy-efficient wireless sensor networks

Existing clustering algorithms of data gathering in wireless sensor networks neglect the impact of event source on the data spatial correlation. In this article, we proposed a compressed sensing–based dynamic clustering algorithm centred on event source. The main challenges of the prescribed scheme are how to model the impact of event source on spatial correlation and how to obtain the location of event source. To solve both the problems, we first formulate the Euclidean distance spatial correlation model and employ joint sparsity model-1 to describe the impact on the spatial correlation caused by event source. Based on these models, we conceive an efficient clustering scheme, which exploits the compressive data for computing the location of event source and for dynamic clustering. Simulation results show that the proposed compressed sensing–based dynamic clustering algorithm centred on event source outperforms the existing data gathering algorithms in decreasing the communication cost, saving the network energy consumption as well as extending the network survival time under a same accuracy. Additionally, the three performance affecting factors, namely, the attenuation coefficient of event sources, the distance between event sources and the number of event sources, are investigated and provided for constituting the application condition of the compressed sensing–based dynamic clustering algorithm centred on event source. The proposed scheme is potential in large-scale wireless sensor networks such as sensor-based IoT application.
Model based code generation for kinematics and dynamics computations in robot controllers

This paper gives an overview on our current research on models and Domain Specific Languages (DSLs) for robotics software development. We target the set of kinematics and dynamics algorithms which are critical for simulation and control (e.g. forward kinematics or inverse dynamics), but whose manual implementation is error prone and time consuming. Our approach relies on model based code generation to address the apparently conflicting requirements arising from the constraints of real time robot controllers on one hand, and the ease of use and the flexibility desirable by the end user on the other hand. Evidences from our experience and numerical experiments demonstrate the effectiveness of this approach.
BRCA 1 and BRCA 2 Hereditary Breast / Ovarian Cancer

Mutations in and are characterized by predisposition to breast cancer and ovarian cancer as well as prostate cancer, colon cancer ( ), and other cancers ( ). The risk of developing cancer that is associated with and cancer-predisposing mutations is not known and appears to be variable even within families of similar ethnic background with the same mutation. Estimates of breast cancer and ovarian cancer risks have been derived from families with multiple affected individuals as well as from families with few affected individuals and from population-based studies. Prognosis for breast cancer survival depends upon the stage at which breast cancer is diagnosed and may not be different among individuals with or cancer-predisposing mutations and controls. Disease characteristics. BRCA1 BRCA2 BRCA1 BRCA2 BRCA1 BRCA2
Changes and trends in remotely-sensed Eco-system Functional Attributes: A pilot assessment with the Google Earth Engine platform

Motivation: Geospatial cloud computing platforms such as Google Earth Engine (GEE) are providing exciting new opportunities to handle, process and model large volumes of Earth Observation (EO) data at speeds much higher than those of desktop-based environment. Despite these advantages, platforms such as GEE are missing required processing algorithms such as a time-series smoothing algorithms (e.g. Whitaker-Anderson). Such routines are important for improving the signal-to-noise ratio in EO-based spectral vegetation indices (VI) time-series used to proxy quantify ecosystem’s primary production dynamics and its spatiotemporal change. Results: The Moving Average Smoother (MAS) preserves more the time-series while the Whittaker Smoother (WS) is less affected by extreme values. We also observed greatest anomalies in the centre-left of the Iberian Peninsula and positive trends in the mountain areas of the Iberian Peninsula. Supplementary information: Supplementary data are available at the end of the report (Appendix).
In vitro and In silico Antigout arthritic activities of Ethanolic and Aqueous stem extracts of Cissus quadrangularis – A TLR 2 and TLR 4 Receptor approach

© 2018 Tharini Palani et al. This is an open access article distributed under the terms of the Creative Commons Attribution License -NonCommercialShareAlikeUnported License (http://creativecommons.org/licenses/by-nc-sa/3.0/). *Corresponding Author Dr. Rajeswary Hari, Department of Biotechnology, Dr. M.G. R. Educational & Research Institute, Maduravoyal, Chennai, India. E-mail: rajihar @ gmail.com In vitro and In silico Antigout arthritic activities of Ethanolic and Aqueous stem extracts of Cissus quadrangularis – A TLR2 and TLR4 Receptor approach
Well Log Estimation Using Genetic Algorithms in a case study , Venezuela

Disponer de un set completo de registros de pozo es de fundamental importancia para la caracterización de un yacimiento. Sin embargo, esto no siempre es posible y se hace necesario recurrir a técnicas de estimación de los registros de pozo faltantes. El método de estimación utilizado en el presente estudio representa el registro a ser estimado en función de los registros existentes utilizando series polinómicas. Un algoritmo genético es usado para optimizar los coeficientes de las series polinómicas. Este modelo no lineal es entrenado en una localidad donde todos los registros de pozo están presentes, y luego, el modelo entrenado es utilizado en una localidad con un registro faltante (registro objetivo).
Improvement and Parameters Optimization of Washout Algorithm in Motion Simulator

The purpose of this paper is to improve the low amplitude signal and get optimal washout algorithm parameters that can accurately provide vehicle motion sensation at high fidelity, within motion platform’s limitations for a motion simulator. To achieve the first goal, an improvement based on the fuzzy strategy is made to enhance its ability to simulate the low amplitude signals. To get the optimal parameters of the washout algorithm, a fitness function is proposed based on the human perception and its correlation coefficients, perception error and displacement of the platform. To ensure the stability of the whole algorithm, the Hurwitz criterion is applied to restrict parameters. To get the optimal solution of the fitness function, the fireworks algorithm is used and can efficiently avoid local extremum. The proposed algorithm is proposed in Matlab/Simulink. The results of Fireworks algorithm show that it is an excellent multi-objective optimization algorithm and can get optimal results. With the results of the improvement algorithm and optimal parameters, we can prove that the method provides better performance in human perception, reference shape tracking and exploiting the platform more efficiently within motion limitation.
Local Remote Lookahead Transfer LC 1 RC 1 RC 2 RC 2 Agent App Code Adaptation Controller Packaging SAMD App Launch Agent App

The collective use of ever connected and pervasive mobile devices has been increasingly sought for in mobile collaboration, such as multiplayer mobile gaming and distributed processing. The current model of mobile collaboration requires each device to install a particular, ‘full’ mobile app for a respective collaboration. Besides, collaboration functionalities are typically implemented at application level. In this paper, we present Single Application Multiple Device (SAMD) as a platformlevel mobile collaboration framework. A mobile app developed using SAMD is capable of fine-grained application sharing. In particular, SAMD enables devices, agreed to participate in collaboration, to get portions of the app on-the-fly and run them without the prior installation. To achieve this, we have developed three solutions as core functionalities of SAMD: 1) Controller packaging, 2) lookahead transfer and 3) code adaptation. We have implemented SAMD on Android as a proof-of-concept prototype. Our experimental results demonstrate SAMD can provide finegrained sharing of latency-insensitive applications.
On the Use of Causality Inference in Designing Tariffs to Implement More Effective Behavioral Demand Response Programs

Providing a price tariff that matches the randomized behavior of residential consumers is one of the major barriers to demand response (DR) implementation. The current trend of DR products provided by aggregators or retailers are not consumer-specific, which poses additional barriers for the engagement of consumers in these programs. In order to address this issue, this paper describes a methodology based on causality inference between DR tariffs and observed residential electricity consumption to estimate consumers’ consumption elasticity. It determines the flexibility of each client under the considered DR program and identifies whether the tariffs offered by the DR program affect the consumers’ usual consumption or not. The aim of this approach is to aid aggregators and retailers to better tune DR offers to consumer needs and so to enlarge the response rate to their DR programs. We identify a set of critical clients who actively participate in DR events along with the most responsive and least responsive clients for the considered DR program. We find that the percentage of DR consumers who actively participate seem to be much less than expected by retailers, indicating that not all consumers’ elasticity is effectively utilized.
Adaptive Learning Rate Selection for Temporal Difference Learning

Temporal Difference (TD) learning is a widely used class of algorithms in reinforcement learning. The success of TD learning algorithms relies heavily on the choice of the learning rate used. In this paper, we use recent results on finite-time performance of TD learning with linear function approximation to design an adaptive learning rate selection rule. The rule comprises a diagnostic test which determines whether the algorithm has reached the steady-state, i.e., the algorithm has stopped learning. Once such a determination is made, the rule adapts the learning rate (reducing the step size) so that the algorithm continues to learn. We implement our proposed rule on a variety of popular reinforcement learning applications and show that in all these scenarios, our rule outperforms the best fixed learning rate as well as other commonly used adaptive learning rate selection rules.
Ontology-based Data Exchange and Integration: an Experience in CyberInfrastructure of Sensor Network Based Monitoring System

— Scientific research has become interdisciplinary and collaborative, of which sharing and utilizing data in an efficient manner is critical. Data collected for environmental monitoring and modeling, however, often lack semantic information vital for efficient data sharing, thereby causing semantic gaps between the data collection and utilization. The problem is especially acute when data have to be processed without human intervention. To support efficient data sharing, this paper proposes an ontology-based architecture to integrate heterogeneous data. With the help of ontology reasoning, it provides a simpler and more intelligent way for data searching with high-precision and high-recall.
Lawrence Berkeley National Laboratory Recent Work Title Reduced-bandwidth multithreaded algorithms for sparse matrix-vector multiplication

On multicore architectures, the ratio of peak memory bandwidth to peak floating-point performance (byte:flop ratio) is decreasing as core counts increase, further limiting the performance of bandwidth limited applications. Multiplying a sparse matrix (as well as its transpose in the unsymmetric case) with a dense vector is the core of sparse iterative methods. In this paper, we present a new multithreaded algorithm for the symmetric case which potentially cuts the bandwidth requirements in half while exposing lots of parallelism in practice. We also give a new data structure transformation, called bitmasked register blocks, which promises significant reductions on bandwidth requirements by reducing the number of indexing elements without introducing additional fill-in zeros. Our work shows how to incorporate this transformation into existing parallel algorithms (both symmetric and unsymmetric) without limiting their parallel scalability. Experimental results indicate that the combined benefits of bitmasked register blocks and the new symmetric algorithm can be as high as a factor of 3.5x in multicore performance over an already scalable parallel approach. We also provide a model that accurately predicts the performance of the new methods, showing that even larger performance gains are expected in future multicore systems as current trends (decreasing byte:flop ratio and larger sparse matrices) continue.
Synopses for XML

All existing proposals for querying XML (e.g., XQuery) rely on a pattern-specification language that allows path navigation and branching through the XML data graph in order to reach the desired data elements. Optimizing such queries depends crucially on the existence of concise synopsis structures that enable accurate compile-time selectivity estimates for complex path expressions over graph-structured XML data. In this paper, we summarize our main results from our recent work on XSKETCHes, a novel approach to building and using statistical summaries of large XML data graphs for effective path-expression selectivity estimation. Our proposed graph-synopsis model exploits localized graph stability to accurately approximate (in limited space) the path and branching distribution in the data graph. To estimate the selectivities of complex path expressions over concise XSKETCH synopses, we develop an estimation framework that relies on appropriate statistical (uniformity and independence) assumptions to compensate for the lack of detailed distribution information. Given our estimation framework, We demonstrate that the problem of building an accuracy-optimal XSKETCH for a given amount of space is NP-hard, and propose an efficient heuristic algorithm based on greedy forward selection. Extensive experimental results with synthetic as well as real-life data sets verify the effectiveness of our approach. To the best of our knowledge, ours is the first work to address this timely problem in the most general setting of graph-structured data and complex (branching) path expressions.
Dynamic Spot Diffusing Channel - A Novel Configuration for Indoor Optical Wireless Communications

Indoor optical wireless links can potentially achieve high bitrates because there is a wide and unregulated bandwidth in the optical spectrum. Moreover, optical wireless links can be implemented using simple and inexpensive devices. However, indoor optical wireless links have their own drawbacks such as limited power due to safety issues and incapability of passing thorough opaque objects, which limit their mobility, range and bandwidth and have prevented them from being used widely in commercial systems. Therefore, there has been much effort to find new configurations for indoor optical wireless links which are able to overcome these limitations. In this thesis, a novel configuration for indoor optical wireless communication, termed the dynamic spot diffusing (DSD) channel, is proposed. In the DSD system, the transmitter sends optical signals to a small moving area on the ceiling termed a spot. The receiver receives reflections of optical signal from the spot when spot is in field of view of the receiver. This configuration is shown to achieve high bitrates and provide a good deal of mobility for users inside the room. In this work, a theoretical model for the DSD channel is proposed and the DSD channel capacity is discussed and computed. Furthermore, the DSD system design is explained and design issues are discussed in order to approach capacity. Finally, using computer simulations, achievable rates inside a room are computed and shown to be close to calculated channel capacity. • .