nils-herrmann/clean_scirepeval huggingface dataset:
Task Scheduling in Cloud Computing : Review

Task scheduling plays a key role in cloud computing systems. Scheduling of tasks cannot be done on the basis of single criteria but under a lot of rules and regulations that we can term as an agreement between users and providers of cloud. This agreement is nothing but the quality of service that the user wants from the providers. Providing good quality of services to the users according to the agreement is a decisive task for the providers as at the same time there are a large number of tasks running at the provider’s side. The task scheduling problem can be viewed as the finding or searching an optimal mapping/assignment of set of subtasks of different tasks over the available set of resources (processors/computer machines) so that we can achieve the desired goals for tasks. In this paper we are performing comparative study of the different algorithms for their suitability, feasibility, adaptability in the context of cloud scenario, after that we try to propose the hybrid approach that can be adopted to enhance the existing platform further. So that it can facilitate cloud-providers to provide better quality of services.  Cloud Computing, Cloud Architecture, Task Scheduling, Scheduling Types, GA, PSO
DIMEx100: A New Phonetic and Speech Corpus for Mexican Spanish

In this paper the phonetic and speech corpus DIMEx100 for Mexican Spanish is presented. We discuss both the linguistic motivation and the computational tools employed for the design, collection and transcription of the corpus. The phonetic transcription methodology is based on recent empirical studies proposing a new basic set of allophones and phonological rules for the dialect of the central part of Mexico. These phonological rules have been implemented in a visualization tool that provides the expected phonetic representation of a text, and also a default temporal alignment between the spoken corpus and its phonetic representation. The tools are also used to compute the properties of the corpus and compare these figures with previous work.
Do phytoestrogens reduce the risk of breast cancer and breast cancer recurrence? What clinicians need to know.

Oestrogen is an important determinant of breast cancer risk. Oestrogen-mimicking plant compounds called phytoestrogens can bind to oestrogen receptors and exert weak oestrogenic effects. Despite this activity, epidemiological studies suggest that the incidence of breast cancer is lower in countries where the intake of phytoestrogens is high, implying that these compounds may reduce breast cancer risk, and possibly have an impact on survival. Isoflavones and lignans are the most common phytoestrogens in the diet. In this article, we present findings from human observational and intervention studies related to both isoflavone and lignan exposure and breast cancer risk and survival. In addition, the clinical implications of these findings are examined in the light of a growing dietary supplement market. An increasing number of breast cancer patients seek to take supplements together with their standard treatment in the hope that these will either prevent recurrence or treat their menopausal symptoms. Observational studies suggest a protective effect of isoflavones on breast cancer risk and the case may be similar for increasing lignan consumption although evidence so far is inconsistent. In contrast, short-term intervention studies suggest a possible stimulatory effect on breast tissue raising concerns of possible adverse effects in breast cancer patients. However, owing to the dearth of human studies investigating effects on breast cancer recurrence and survival the role of phytoestrogens remains unclear. So far, not enough clear evidence exists on which to base guidelines for clinical use, although raising patient awareness of the uncertain effect of phytoestrogens is recommended.
Hybrid gate dielectric materials for unconventional electronic circuitry.

Recent advances in semiconductor performance made possible by organic π-electron molecules, carbon-based nanomaterials, and metal oxides have been a central scientific and technological research focus over the past decade in the quest for flexible and transparent electronic products. However, advances in semiconductor materials require corresponding advances in compatible gate dielectric materials, which must exhibit excellent electrical properties such as large capacitance, high breakdown strength, low leakage current density, and mechanical flexibility on arbitrary substrates. Historically, conventional silicon dioxide (SiO2) has dominated electronics as the preferred gate dielectric material in complementary metal oxide semiconductor (CMOS) integrated transistor circuitry. However, it does not satisfy many of the performance requirements for the aforementioned semiconductors due to its relatively low dielectric constant and intransigent processability. High-k inorganics such as hafnium dioxide (HfO2) or zirconium dioxide (ZrO2) offer some increases in performance, but scientists have great difficulty depositing these materials as smooth films at temperatures compatible with flexible plastic substrates. While various organic polymers are accessible via chemical synthesis and readily form films from solution, they typically exhibit low capacitances, and the corresponding transistors operate at unacceptably high voltages. More recently, researchers have combined the favorable properties of high-k metal oxides and π-electron organics to form processable, structurally well-defined, and robust self-assembled multilayer nanodielectrics, which enable high-performance transistors with a wide variety of unconventional semiconductors. In this Account, we review recent advances in organic-inorganic hybrid gate dielectrics, fabricated by multilayer self-assembly, and their remarkable synergy with unconventional semiconductors. We first discuss the principals and functional importance of gate dielectric materials in thin-film transistor (TFT) operation. Next, we describe the design, fabrication, properties, and applications of solution-deposited multilayer organic-inorganic hybrid gate dielectrics, using self-assembly techniques, which provide bonding between the organic and inorganic layers. Finally, we discuss approaches for preparing analogous hybrid multilayers by vapor-phase growth and discuss the properties of these materials.
Opponent actor learning (OpAL): modeling interactive effects of striatal dopamine on reinforcement learning and choice incentive.

The striatal dopaminergic system has been implicated in reinforcement learning (RL), motor performance, and incentive motivation. Various computational models have been proposed to account for each of these effects individually, but a formal analysis of their interactions is lacking. Here we present a novel algorithmic model expanding the classical actor-critic architecture to include fundamental interactive properties of neural circuit models, incorporating both incentive and learning effects into a single theoretical framework. The standard actor is replaced by a dual opponent actor system representing distinct striatal populations, which come to differentially specialize in discriminating positive and negative action values. Dopamine modulates the degree to which each actor component contributes to both learning and choice discriminations. In contrast to standard frameworks, this model simultaneously captures documented effects of dopamine on both learning and choice incentive-and their interactions-across a variety of studies, including probabilistic RL, effort-based choice, and motor skill learning.
Numerical Algebraic Geometry for Optimal Control Applications

A new technique for solving polynomial nonlinear constrained optimal control problems is presented. The problem is reformulated into a parametric optimization problem, which in turn is solved in a two step procedure. First, in a pre-computation step, the equation part of the corresponding first order optimality conditions is solved for a generic value of the parameter. Relying on the underlying algebraic geometry, this first solution makes it possible to solve efficiently and in real time the corresponding optimal control problem at the measured parameter value for each subsequent time step. This approach has a probability one guarantee of finding the global optimal solution at each step. Controller synthesis for two applications from the area of power electronics featuring a dc-ac converter and a dc-dc converter are discussed to motivate the proposed approach.
First Step Towards End-to-End Parametric TTS Synthesis: Generating Spectral Parameters with Neural Attention

In conventional neural networks (NN) based parametric text-tospeech (TTS) synthesis frameworks, text analysis and acoustic modeling are typically processed separately, leading to some limitations. On one hand, much significant human expertise is normally required in text analysis, which presents a laborious task for researchers; on the other hand, training of the NN-based acoustic models still relies on the hidden Markov model (HMM) to obtain frame-level alignments. This acquisition process normally goes through multiple complicated stages. The complex pipeline makes constructing a NN-based parametric TTS system a challenging task. This paper attempts to bypass these limitations using a novel end-to-end parametric TTS synthesis framework, i.e. the text analysis and acoustic modeling are integrated together employing an attention-based recurrent neural network. Thus the alignments can be learned automatically. Preliminary experimental results show that the proposed system can generate moderately smooth spectral parameters and synthesize fairly intelligible speech on short utterances (less than 8 Chinese characters).
Think Big with Big Data: Identifying Suitable Big Data Strategies in Corporate Environments

Businesses increasingly attempt to learn more about their customers, suppliers, and operations by using millions of networked sensors integrated, for example, in mobile phones, cashier systems, automobiles, or weather stations. This development raises the question of how companies manage to cope with these ever-increasing amounts of data, referred to as Big Data. Consequently, the aim of this paper is to identify different Big Data strategies a company may implement and provide a set of organizational contingency factors that influence strategy choice. In order to do so, we reviewed existing literature in the fields of Big Data analytics, data warehousing, and business intelligence and synthesized our findings into a contingency matrix that may support practitioners in choosing a suitable Big Data approach. We find that while every strategy can be beneficial under certain corporate circumstances, the hybrid approach - a combination of traditional relational database structures and MapReduce techniques - is the strategy most often valuable for companies pursuing Big Data analytics.
Scalable and flexible Max-Var generalized canonical correlation analysis via alternating optimization

Unlike dimensionality reduction (DR) tools for single-view data, e.g., principal component analysis (PCA), canonical correlation analysis (CCA) and generalized CCA (GCCA) are able to integrate information from multiple feature spaces of data. This is critical in multi-modal data fusion and analytics, where samples from a single view may not be enough for meaningful DR. In this work, we focus on a popular formulation of GCCA, namely, MAX-VAR GCCA. The classic MAX-VAR problem is optimally solvable via eigen-decomposition, but this solution has serious scalability issues. In addition, how to impose regularizers on the sought canonical components was unclear - while structure-promoting regularizers are often desired in practice. We propose an algorithm that can easily handle datasets whose sample and feature dimensions are both large by exploiting data sparsity. The algorithm is also flexible in incorporating regularizers on the canonical components. Convergence properties of the proposed algorithm are carefully analyzed. Numerical experiments are presented to showcase its effectiveness.
Body shape by 3-D photonic scanning in Thai and UK adults: comparison of national sizing surveys

Body mass index (BMI) cut-offs associated with increased risk of diabetes and cardiovascular disease differ between European and Asian populations, and among Asian populations. Within-population and ethnic variability in body shape has likewise been linked with variability in cardiovascular risk in western settings.To explore differences between Thai and White UK adults in body shape and its associations with height, age and BMI.Methods:Data on weight and body shape by 3-D photonic scanning from National Sizing Surveys of UK (3542 men, 4130 women) and Thai (5889 men, 6499 women) adults aged 16–90 years, using a common protocol and methodology, were analysed.Thai adults in both sexes had significantly smaller body girths than UK adults after adjusting for age and height. Matching for BMI, and adjusting for height and age, Thais in both sexes tended to have similar or greater limb girths, but significantly smaller torso girths (especially waist and hip) than UK individuals. These results were replicated within narrow BMI bands at ∼20 and ∼25 kg m−2. Shape-age associations also differed between the populations.Young Thai adults have a significantly slighter physique than White UK adults, with a less central distribution of body weight. However these differences reduce with age, especially in males. The 3-D photonic scanning provides detailed digital anthropometric data capable of monitoring between- and within-individual shape variability. The technology merits further application to investigate whether variability in body shape is more sensitive to metabolic risk than BMI within and between-populations.
Large-Scale Visual Speech Recognition

This work presents a scalable solution to open-vocabulary visual speech recognition. To achieve this, we constructed the largest existing visual speech recognition dataset, consisting of pairs of text and video clips of faces speaking (3,886 hours of video). In tandem, we designed and trained an integrated lipreading system, consisting of a video processing pipeline that maps raw video to stable videos of lips and sequences of phonemes, a scalable deep neural network that maps the lip videos to sequences of phoneme distributions, and a production-level speech decoder that outputs sequences of words. The proposed system achieves a word error rate (WER) of 40.9% as measured on a held-out set. In comparison, professional lipreaders achieve either 86.4% or 92.9% WER on the same dataset when having access to additional types of contextual information. Our approach significantly improves on other lipreading approaches, including variants of LipNet and of Watch, Attend, and Spell (WAS), which are only capable of 89.8% and 76.8% WER respectively.
Simulation of bubbles

We present a novel framework based on a continuous fluid simulator for general simulation of realistic bubbles, with which we can handle as many significant dynamic bubble effects as possible. To capture nature of the very thin liquid film of bubbles, we have developed a regional level set method allowing multi-manifold interface tracking. The regional level set method is based on the definitions of regional distance and its five operators, which makes it very easy to implement. We can reconstruct an implicit surface of liquid film with arbitrary thickness from the representation of regional level set functions. To overcome the numerical instability caused by surface tension, we exploit a new semi-implicit surface tension model which is unconditionally stable and makes the simulation of surface tension dominated phenomena much more efficient. An approximated film thickness evolution model is proposed to control the bubble's lifecycle. All these new techniques combine into a general framework that can produce various realistic dynamic effects of bubbles.
Effectiveness of the BT mite trap for detecting the storage mite pests, Acarus siro, Lepidoglyphus destructor and Tyrophagus longior

Traps have been used extensively to provide early warning of hidden pest infestations. To date, however, there is only one type of trap on the market in the U.K. for storage mites, namely the BT mite trap, or monitor. Laboratory studies have shown that under the test conditions (20 °C, 65% RH) the BT trap is effective at detecting mites for at least 10 days for all three species tested: Lepidoglyphus destructor, Tyrophagus longior and Acarus siro. Further tests showed that all three species reached a trap at a distance of approximately 80 cm in a 24 h period. In experiments using 100 mites of each species, and regardless of either temperature (15 or 20 °C) or relative humidity (65 or 80% RH), the most abundant species in the traps was T. longior, followed by A. siro then L. destructor. Trap catches were highest at 20 °C and 65% RH. Temperature had a greater effect on mite numbers than humidity. Tests using different densities of each mite species showed that the number of L. destructor found in/on the trap was significantly reduced when either of the other two species was dominant. It would appear that there is an interaction between L. destructor and the other two mite species which affects relative numbers found within the trap.
Anthropology in the Clinic: The Problem of Cultural Competency and How to Fix It

October 2006 | Volume 3 | Issue 10 | e294 Cultural competency has become a fashionable term for clinicians and researchers. Yet no one can defi ne this term precisely enough to operationalize it in clinical training and best practices. It is clear that culture does matter in the clinic. Cultural factors are crucial to diagnosis, treatment, and care. They shape health-related beliefs, behaviors, and values . But the large claims about the value of cultural competence for the art of professional care-giving around the world are simply not supported by robust evaluation research showing that systematic attention to culture really improves clinical services. This lack of evidence is a failure of outcome research to take culture seriously enough to routinely assess the cost-effectiveness of culturally informed therapeutic practices, not a lack of effort to introduce culturally informed strategies into clinical settings .
BEAST 2: A Software Platform for Bayesian Evolutionary Analysis

We present a new open source, extensible and flexible software platform for Bayesian evolutionary analysis called BEAST 2. This software platform is a re-design of the popular BEAST 1 platform to correct structural deficiencies that became evident as the BEAST 1 software evolved. Key among those deficiencies was the lack of post-deployment extensibility. BEAST 2 now has a fully developed package management system that allows third party developers to write additional functionality that can be directly installed to the BEAST 2 analysis platform via a package manager without requiring a new software release of the platform. This package architecture is showcased with a number of recently published new models encompassing birth-death-sampling tree priors, phylodynamics and model averaging for substitution models and site partitioning. A second major improvement is the ability to read/write the entire state of the MCMC chain to/from disk allowing it to be easily shared between multiple instances of the BEAST software. This facilitates checkpointing and better support for multi-processor and high-end computing extensions. Finally, the functionality in new packages can be easily added to the user interface (BEAUti 2) by a simple XML template-based mechanism because BEAST 2 has been re-designed to provide greater integration between the analysis engine and the user interface so that, for example BEAST and BEAUti use exactly the same XML file format.
COBRA: A framework for the analysis of realistic mobility models

The future global Internet is going to have to cater to users that will be largely mobile. Mobility is one of the main factors affecting the design and performance of wireless networks. Mobility modeling has been an active field for the past decade, mostly focusing on matching a specific mobility or encounter metric with little focus on matching protocol performance. This study investigates the adequacy of existing mobility models in capturing various aspects of human mobility behavior (including communal behavior), as well as network protocol performance. This is achieved systematically through the introduction of a framework that includes a multi-dimensional mobility metric space. We then introduce COBRA, a new mobility model capable of spanning the mobility metric space to match realistic traces. A methodical analysis using a range of protocol (epidemic, spraywait, Prophet, and Bubble Rap) dependent and independent metrics (modularity) of various mobility models (SMOOTH and TVC) and traces (university campuses, and theme parks) is done. Our results indicate significant gaps in several metric dimensions between real traces and existing mobility models. Our findings show that COBRA matches communal aspect and realistic protocol performance, reducing the overhead gap (w.r.t existing models) from 80% to less than 12%, showing the efficacy of our framework.
Interpolation of orientation distribution functions in diffusion weighted imaging using multi-tensor model

Diffusion weighted imaging (DWI) is a non-invasive method for investigating the brain white matter structure and can be used to evaluate fiber bundles. However, due to practical constraints, DWI data acquired in clinics are low resolution.


NEW METHOD
This paper proposes a method for interpolation of orientation distribution functions (ODFs). To this end, fuzzy clustering is applied to segment ODFs based on the principal diffusion directions (PDDs). Next, a cluster is modeled by a tensor so that an ODF is represented by a mixture of tensors. For interpolation, each tensor is rotated separately.



The method is applied on the synthetic and real DWI data of control and epileptic subjects. Both experiments illustrate capability of the method in increasing spatial resolution of the data in the ODF field properly. The real dataset show that the method is capable of reliable identification of differences between temporal lobe epilepsy (TLE) patients and normal subjects.


COMPARISON WITH EXISTING 
The method is compared to existing methods. Comparison studies show that the proposed method generates smaller angular errors relative to the existing methods. Another advantage of the method is that it does not require an iterative algorithm to find the tensors.



The proposed method is appropriate for increasing resolution in the ODF field and can be applied to clinical data to improve evaluation of white matter fibers in the brain.
Design investigation of dual band H-plane SIW horn antenna with elliptical shaped radiating aperture

In this paper a dual band substrate integrated waveguide H-plane horn antenna is proposed. The radiating aperture of modified antenna is half elliptical shaped with specified major and minor radius. To improve backward radiation and directivity of the proposed antenna, a reflector plate is located around the radiating aperture. The proposed antennas are numerically investigated using High Frequency Structure Simulator (HFSS) software. Radiation performance of the modified horn is presented and compared with those of the conventional horn. Results illustrate that proposed structures provide directivity of 10 dB at the first band, which is 3.1 dB more than directivity of the conventional horn. For the other band, antenna directivity is enhanced up to 7.36 dB.