Miracle-dz/UniFAR-FTUs huggingface dataset:
On testing models for the pressure-strain correlation of turbulence using direct simulations

Direct simulations of homogeneous turbulence have, in recent years, come into widespread use for the evaluation of models for the pressure–strain correlation of turbulence. While work in this area has been beneficial, the increasingly common practice of testing the slow and rapid parts of these models separately in uniformly strained turbulent flows is shown in this paper to be unsound. For such flows, the decomposition of models for the total pressure–strain correlation into slow and rapid parts is ambiguous. Consequently, when tested in this manner, misleading conclusions can be drawn about the performance of pressure–strain models. This point is amplified by illustrative calculations of homogeneous shear flow where other pitfalls in the evaluation of models are also uncovered. More meaningful measures for testing the performance of pressure–strain models in uniformly strained turbulent flows are proposed and the implications for turbulence modeling are discussed.
A Novel Classification Optimization Approach Integrating Class Adaptive MRF and Fuzzy Local Information for High Spatial Resolution Multispectral Imagery

This paper develops a novel classification optimization approach integrating class adaptive Markov Random Field (MRF) and fuzzy local information (CAMRF-FLI) for high spatial resolution multispectral imagery (HSRMI). Firstly, the raw classification results, including initial fuzzy memberships and class labels of every pixel, are achieved by a pixel-wise classification method for a given image. Secondly, the class adaptive MRF-based data energy function is developed to integrate class spatial dependency information. Thirdly, a novel spatial energy function integrating fuzzy local information is constructed. Finally, based on the total of data and spatial energies, the raw classification map is regularized by a global minimization of the energy function using its iterated conditional modes (ICM). The effectiveness of CAMRF-FLI is performed by two data sets. The results indicate it can refine the classification map in homogeneous areas, meanwhile, reduce most of the edge blurring artifact, and improve the classification accuracy compared with some conventional approaches.
Performance of GTX Titan X GPUs and Code Optimization

Recently Nvidia has released a new GPU model: GTX Titan X (TX) in a linage of the Maxwell architecture. We use our conjugate gradient code and non-perturbative renormalization code to measure the performance of TX. The results are compared with those of GTX Titan Black (TB) in a lineage of the Kepler architecture. We observe a significant gain in the single and double precision calculations much greater than the theoretical expectation.
From Traditional Farming in Morocco to Early Urban Agroecology in Northern Mesopotamia: Combining Present-day Arable Weed Surveys and Crop Isotope Analysis to Reconstruct Past Agrosystems in (Semi-)arid Regions

ABSTRACT We integrate functional weed ecology with crop stable carbon and nitrogen isotope analysis to assess their combined potential for inferring arable land management practices in (semi-)arid regions from archaeobotanical assemblages. Weed and GIS survey of 60 cereal and pulse fields in Morocco are combined with crop sampling for stable isotope analysis to frame assessment of agricultural labour intensity in terms of manuring, irrigation, tillage and hand-weeding. Under low management intensity weed variation primarily reflects geographical differences, whereas under high management intensity fields in disparate regions have similar weed flora. Manured and irrigated oasis barley fields are clearly discriminated from less intensively manured rain-fed barley terraces in southern Morocco; when fields in northern and southern Morocco are considered together, climatic differences are superimposed on the agronomic intensity gradient. Barley δ13C and δ15N values clearly distinguish among the Moroccan regimes. An integrated approach combines crop isotope values with weed ecological discrimination of low- and high-intensity regimes across multiple studies (in southern Morocco and southern Europe). Analysis of archaeobotanical samples from EBA Tell Brak, Syria suggests that this early city was sustained through extensive (low-intensity, large-scale) cereal farming.
Threads Shared Memories L 1 Cache Read-only Data Cache L 2 Cache DRAM

This paper presents the experimental evaluation of a new data mapping technique for the GPU shared memory, called Adaptive Modular Mapping (AMM). The evaluated technique aims to remap data across the shared memory physical banks, so as to increase parallel accesses, resulting in appreciable gains in terms of performance. Unless previous techniques described in literature, AMM does not increase shared memory size as a side effect of the conflict-avoidance technique. The paper also presents the experimental set-up used for the validation of the proposed memory mapping methodology.
Libraries Resource Directory CONSUMPTION OF AMMONIA

This study investigated the performance of bio ammonia oxidizing bacteria (AOB) to assess the consumption of ammonia conducted in a one litre reactor.The reactor consisted of bio substrate for the nitrifying bacteria.Low concentration of NH and high concentrations (between 35 and 100 mg/L) were used in this batch culture experiment.Simultaneously, the production of nitrate consumption of nitrite-nitrogen by the NOB.The results showed that the nitrifying bacteria in the bio-filters were able to reduce the NH also found that the highest removal rate of NH
The Real Effective Exchange Rate Impact on ASEAN-5 Economic Growth

This is an empirical analysis study focuses simultaneously on the impact of the real effective exchange rate, domestic interest rate, foreign interest rate, inflation rate, domestic money supply, net foreign assets and terms of trade which were identified by earlier researchers upon real growth domestic products (RGDP) of the ASEAN-5 countries. The real effective exchange rates (REER) of these currencies are measured against the US Dollar based on authoritative quarterly data time series between 1991:Q1 to 2006:Q2. This study is expected to provide several important economic and financial contributions to fill the gap in the financial and economic literature for ASEAN countries. It identifies the measure of the impact of the real effective exchange rates (REER) upon their economic growth using the most advanced statistical frameworks. A recently developed “Autoregressive Distributed Lag (ARDL)” co-integration by Pesaran et al. was the fundamental approach employed to analyse the relevant hypotheses of this study. The ARDL technique indeed was recognized to have additional advantages of yielding consistent estimates of the long-run coefficient. The empirical results confirm that the domestic money supply (M1) followed by REER (S) are the long-term and short- run variables that had positive and significant impact on ASEAN-5 countries’ real domestic products’ growth rate (RGDP). The other identified variables by the earlier researchers had diverse impacts during the full study period, their impact on economic growth of the study countries over the period vary in accordance to the contemporary businesses and economics environments.
Framework for a Lottery-Based Incentive Scheme and its Influence on Commuting Behaviors-an MIT Case Study

Congestion pricing has been widely regarded as the most effective technique used to manage travel demand. Since congestion pricing is about charging road users to internalize the external cost induced by their travels, there have been concerns about the social equity issues it raises and the political opposition it often faces. This thesis focuses on another travel demand management strategy that is contrary to congestion pricing, a reward-based scheme which provides positive incentives to commuters when they change their travel behavior. More specifically, commuters could be rewarded credits when they travel to work via walking, bicycling, taking public transit or carpooling instead of driving alone. The credits accrued can either be used to exchange for a deterministic cash prize, or a chance to enter a grand lottery draw. This thesis reviews the research literature to explain the influence of lottery schemes on behaviors and modifies a theoretical model to suggest how equilibrium mode choices can be influenced by lotteries. A general framework to design such a lottery scheme is proposed and, based on the framework, several alternatives to implement a lottery-based incentive scheme to manage commuter parking demand at MIT are developed and evaluated. Thesis Supervisor: John P. Attanucci Title: Senior Lecturer and Research Associate of Civil and Environmental Engineering
Communities of practice and ‘student voice’: engaging with student representatives at the faculty level

Student engagement in institutional governance and curriculum development is an important and valued aspect of higher education (HE). In the UK, changes to funding and quality assurance arrangements have led to concerns that a consumerist model is developing, with implied changes to the relationship between students and universities. The National Union of Students has responded by calling for reframing these relationships as communities of practice (Streeting and Wise 2009) and more recently as a clearly defined partnership (Wenstone 2012). This article applies the notion of communities of practice (Wenger 2001) to one aspect of student governance, through a project that revitalised the way one university faculty worked with student representatives (O’Hara and Flint 2010). A critical analysis of the project, exploring how it promoted communities of practice, provides valuable insights and recommendations for engaging students with institutional governance. Our analysis highlights the limitations and complexity of applying this framework in practice and recommends integration with complementary scholarship around student voice and partnerships.
Paramedic independent prescribing: a qualitative study of early adopters in the UK

Background: Paramedics working in advanced practice roles in the UK can now train to prescribe medicine. This is anticipated to benefit patient access to medicines and quality of care where there is a national shortage of doctors, particularly in primary care. Aim: To explore the experience of paramedics who are early adopters of independent prescribing in a range of healthcare settings in the UK. Design and setting: A qualitative study involving interviews between May and August 2019, with paramedics in the UK who had completed a prescribing programme. Methods: Individual interviews with a purposive sample of paramedics recruited via social media and regional paramedic networks. Interviews covered experiences, benefits and challenges of the prescribing role. A framework analysis approach was used to identify key themes. Results: Participants were 18 advanced paramedics working in primary care, emergency departments, urgent care centres and rapid response units. All participants reported being adequately prepared to prescribe. Key benefits of prescribing included improving service capacity, efficiency and safety, and facilitating advanced clinical roles. Challenges included technological problems, inability to prescribe controlled drugs and managing expectations about the prescribing role. Concerns were raised about support and role expectations, particularly in general practice. Conclusion: Paramedic prescribing is most successful in settings with a high volume of same-day presentations and urgent and emergency care. It facilitated advanced roles within multidisciplinary teams. Concerns indicate that greater consideration for support infrastructure and workforce planning is required within primary care to ensure paramedics meet the entry criteria for a prescribing role.
Gem5-X: A Gem5-Based System Level Simulation Framework to Optimize Many-Core Platforms

The rapid expansion of online-based services requires novel energy and performance efficient architectures to meet power and latency constraints. Fast architectural exploration has become a key enabler in the proposal of architectural innovation. In this paper, we present gem5-X, a gem5-based system level simulation framework, and a methodology to optimize many-core systems for performance and power. As real-life case studies of many-core server workloads, we use real-time video transcoding and image classification using convolutional neural networks (CNNs). Gem5-X allows us to identify bottlenecks and evaluate the potential benefits of architectural extensions such as in-cache computing and 3D stacked High Bandwidth Memory. For real-time video transcoding, we achieve 15% speed-up using in-order cores with in-cache computing when compared to a baseline in-order system and 76% energy savings when compared to an Out-of-Order system. When using HBM, we further accelerate real-time transcoding and CNNs by up to 7% and 8% respectively.
HPI-DHC at TREC 2018 Precision Medicine Track

Abstract —The TREC-PM challenge aims for advances in the ﬁeld of information retrieval applied to precision medicine. Here we describe our experimental setup and the achieved results in its 2018 edition. We explored the use of unsupervised topic models, supervised document classiﬁcation, and rule-based query-time search term boosting and expansion. We participated in the biomedical articles and clinical trials subtasks and were among the three highest-scoring teams. Our results showed that query expansion associated with hand-crafted rules contribute to better values of information retrieval metrics. However, the use of a precision medicine classiﬁer did not show the expected improvement for the biomedical abstracts subtask. In the future, we plan to add different terminologies to replace hand-crafted rules and experiment with negation detection.
Investigating Models for Overdispersed Count Data and its Applications

Count data often exhibit more variation than what is assumed by Poisson distribution. This is known as over-dispersion. [1] When over-dispersion is present, standard errors of estimated parameters are underestimated. Several models and standard error estimators have been developed to accommodate this phenomenon. This paper seeks to discuss four of them; the quasi-Poisson regression model, negative binomial regression model, Poisson regression model with robust standard errors and Poisson regression model with bootstrap standard errors. A simulation study is conducted to compare prediction errors and standard errors obtained from implementing the models and standard error estimators discussed. Results showed similar prediction errors and standard errors for all methods discussed. We then present an example of modeling diamond prices and provide estimates of parameters and standard errors using models and standard error estimators discussed. Results indicated similar parameter estimates for the quasi-Poisson regression model, Poisson regression model with robust standard errors and, Poisson regression model with bootstrap standard errors but not the negative binomial regression and similar standard errors for the quasi-Poisson regression model, negative binomial regression model, and Poisson regression model with bootstrap standard errors but not the Poisson regression model with robust standard errors.
Systematic Review of Trends and Gaps in Collaborative Software Engineering in the Cloud

This paper presents a review of trends and challenges in collaborative Software Engineering. Due to the nature and size of large-scale Software Engineering projects, effective collaboration is important and necessary. Hence, it is not uncommon to see the adoption of a remix of practices, models, methodologies, tools and skills. However, this remix, alongside adoption of emerging paradigms such as Cloud computing, results in factors that undermine collaborative Software Engineering projects. This paper aims to provide a systematic review and analysis of existing trends, models and challenges. This is with a view towards fostering better understanding of factors undermining the collaborative Software Engineering process, as well as, helps to identify motivations, gaps, and issues pertinent to this research area for a more effective process in the Cloud. A systematic approach was employed in this research. This approach is instrumental to identifying relevant primary studies. Its design provides a means for continuity in terms of any future extension to this review. Keywords—Collaborative software engineering; software development process; models; trends; cloud computing; collaboration; systematic review
Investigation of Deep Convolutional Neural Network (CNN) approaches’ accuracy for the detection of COVID-19

Abstract— the world those days focuses on protecting human health and combating the irruption of coronavirus patients (COVID-19). As results of its extra ordinarily contagious infection that have caused a disturbance in everyone's lives in various ways. For early screening, Reverse Transcription Protein Chain Reaction (RT-PCR) test is used to examine the onset of the patients by detecting the RNA material of the virus among the patients’ samples. Recent results indicate that the applying of X-ray images and X-radiation (CT) improves the detection accuracy of this disease. However, the classification task of medical images is tough due to several factors such as lack of dataset for COVID-19, and difficulty in identifying type of infection. Recent research works have been proposed for COVID-19 detection that has been applied on specific datasets. Thus, it is vital to validate their performance on various datasets with different imaging disease conditions. The paper presents a comparison study between top performer CNN models that recorded the very best detection accuracy in image detection and classification: COVID-Net, VGG16, ResNet, Bayesian, DenseNet, and DarkNet. Such CNN approaches can assist medical staff in the early detection of infection. Additionally, we improved dataset in terms of quality, clarity, and quantity using augmentation technique. The quantitative results show that Darknet and COVID-net yield high detection accuracy when applied on CT and X-ray dataset. We validated our results by training the models on multiple different datasets, using CPU and GPU with various bach sizes and optimizers.
Note on the weekly cycle of storm heights over the southeast United States

[1] An earlier paper by Bell et al. (2008) showed satellite evidence that average summertime (1998–2005) rainfall over the noncoastal southeast U.S. varied with the day of the week in a statistically significant way, with the maximum occurring midweek (Tuesday–Thursday). An explanation was proposed in which the recurring midweek increase in air pollution over the area causes a shift in the drop size distribution in clouds to smaller sizes as the clouds develop. The smaller droplets could be carried to higher altitudes where their freezing releases additional latent heat, invigorating the storms. Evidence for this phenomenon was provided by storm height distributions obtained from the Tropical Rainfall Measuring Mission radar, but the statistical significance of the midweek increase in storm heights was unclear. An improved statistical analysis of the storm height distributions is provided here, indicating that the probability that storms climb above altitudes of 7–15 km is increased midweek relative to weekends (Saturday– Monday) for afternoon storms (1200–2400 LT). The morning storm heights, on the other hand, are found not to exhibit statistically significant shifts, which would be consistent with the above explanation. Morning storm statistics are also found to be much more sensitive than afternoon storm statistics to the exact area over which the averages are taken.
Exploring the Coaction of Motivation and Learning Styles in Educational Gamification: A Preliminary Study for Understanding 'Motivational Learning Modes'

This study explores the different motivations and learning styles of students using a game for revision in a leading university. The research is unique in attempted to understand the coaction of motivation and learning style through rich qualitative empirical work, which unpacks the opinion of game users and their inherent real-life experience of educational gamification and associated game elements. Our findings indicate that there are three specific modes of interaction between motivation and learning which we call ‘motivational learning modes’ . As this is a preliminary study, we conclude by detailing our future work and fruitful avenues for expanding educational gamification research based on our insights.
Opinion Formation Dynamics with Contrarians and Zealots

Mean-field type ODE models for opinion dynamics often assume that the entire population is comprised of congregators, who are agreeable. On the other hand, a contrarian opinion dynamics ODE model assumes the population has two personality types: congregators, and contrarians, who are disagreeable. In this paper we broadly study how contrarians influence the ability of the population to form a fixed and stable opinion. In particular, we re-examine the dynamics associated with the model introduced by Tanabe and Masuda [12] by looking at how the parameters effect the formation of stable periodic solutions (whose existence implies there is no fixed consensus opinion). Afterwards, we refine and analyze the model under two new hypotheses: (a) the contrarians bow to peer pressure and change their personality type to congregators if a large enough proportion of the entire population agrees on an opinion, and (b) there are zealots associated with one of the opinions. We conclude with a brief discussion on possible extensions of this work.
Simulation of the Optical Properties of Gold Nanorods : Comparison to Experiment

The optical absorption spectra of the gold nanorods embedded in a dielectric medium are simulated using finite element method. The study is achieved for various shapes and forms of the nanoparticles and for different values of the dielectric constant of the host medium. Also, we quantify the influence of the aspect ratio and the dielectric constant of the surrounding medium on both longitudinal and transversal resonance modes. It is found that the obtained results are in good agreement with a previous theoretical work that is based on the classical electrostatic predictions and assuming that the gold nanorods behave as ellipsoidal particles. Finally, a comparison of the predicted results with experimental data dealt with the same system is very satisfactory.
Gradient-Based Photorealistic Rendering of Rain Streaks for Image Processing Applications

To record criminal activities who loot company properties in various places such as parking lots, outside the work place, banks, industries etc., surveillance systems are installed. Under aggressive and bad weather conditions, the images recorded contains rain streaks and are difficult to conceptualize. In these situations, images will help the police in investigation. Removal of rain droplets from still images has been an effective research topic for various image processing applications. In this paper, to the best of our knowledge, we use Histogram Oriented Gradient (HOG) to extract rain droplets from an image. After extraction, K-means clustering and SVM classifiers removes the common rain patterns present in the image. Finally, the performance is evaluated by calculating the PSNR value for original image and comparing the values with the PSNR of rain removed image, which are more or less found to be equal.