Miracle-dz/UniFAR-FTUs huggingface dataset:
Cell volume control at a surface for three-dimensional grid generation packages

An alternate method of calculating the cell size for orthogonality control in the solution of Poisson's 3D space equations is presented. The method provides the capability to enforce a better initial guess for the grid distribution required for boundary layer resolution. This grid point distribution is accomplished by enforcing grid spacing from a grid block boundary where orthogonality is required. The actual grid spacing or cell size for that boundary is determined by the two or four adjacent boundaries in the grid block definition, which are two dimensional grids. These two dimensional grids are in turn defined by the user using insight into the flow field and boundary layer characteristics. The adjoining boundaries are extended using a multifunctional blending scheme, with user control of the blending and interpolating functions to be used. This grid generation procedure results in an enhanced computational fluid dynamics calculation by allowing a quicker resolution of the configuration's boundary layer and flow field and by limiting the number of grid re-adaptations. The cell size specification calculation was applied to a variety of configurations ranging from axisymmetric to complex three-dimensional configurations. Representative grids are shown for the Space Shuttle and the Langley Lifting Body (HL-20).
Improved single image and video dehazing using morphological operation

In the hazy days, the contrast is reduced by the distance, which obstruct the outdoor surveillance system from working properly. In this paper, we present an effective approach for defogging the images and videos based on filtering method. We first obtain an initial atmospheric scattering light by using gray scale morphological operation and refine it using guided filter. Finally the scene radiance is corrected using the visibility restoration model. The experimental results demonstrated that this method can effectively remove the bad weather condition and enhance the contrast of the input images. The use of gray scale morphological operation of these images makes our approach faster and it takes only 80% of the time in execution as compared with the fast bilateral filter. Present algorithm, due to its speed and ability to improve visibility, can be used in many systems, ranging from tracking and navigation, surveillance, consumer electronics, intelligent vehicles to remote sensing.
The reverse transcriptase sequence of human immunodeficiency virus type 1 is under positive evolutionary selection within the central nervous system

The human immunodeficiency virus type 1 (HIV-1) enters the central nervous system (CNS) during the acute phase of infection and causes AIDS-related encephalitis and dementia in 30% of individuals. Previous studies show that HIV-1 sequences derived from the CNS of infected patients, including the sequence encoding reverse transcriptase (RT), are genetically distinct from sequences in other tissues. The hypothesis of the current study is that the RT sequence of HIV-1 is under positive selection within the CNS. Multiple alignments of non-CNS-derived and CNS-derived HIV-1 RT sequences were constructed using the ClustalW 1.8 program. The multiple alignments were analyzed with the Synonymous/Nonsynonymous Analysis Program. Codon positions 122–125, 135–149, and 166–212 of the CNS-derived RT sequences underwent a greater accumulation of nonsynonymous than synonymous substitutions, which was markedly different from the analysis results of the non-CNS-derived RT sequences. These residues are located in the finger and palm subdomains of the RT protein structure, which encodes the polymerase active site. The analysis of CNS-derived partial-length RT sequences that encompass these regions yielded similar results. A comparison of CNS-derived RT sequences to a non-CNS-derived RT consensus sequence revealed that a majority of the nonsynonymous substitutions resulted in a specific amino acid replacement. These results indicate that reverse transcriptase is under positive selection within the CNS. The amino acid replacements were visualized on a three-dimensional structure of HIV-1 RT using the Sybyl software suite. The protein structure analysis revealed that the amino acid replacements observed among the CNS-derived sequences occurred in areas of known structural and functional significance.
Rattle: A Data Mining GUI for R

Data mining delivers insights, pat- terns, and descriptive and predictive models from the large amounts of data available today in many organisations. The data miner draws heavily on methodologies, techniques and al- gorithms from statistics, machine learning, and computer science. R increasingly provides a powerful platform for data mining. However, scripting and programming is sometimes a chal- lenge for data analysts moving into data mining. The Rattle package provides a graphical user in- terface specifically for data mining using R. It also provides a stepping stone toward using R as a programming language for data analysis.
A Cloud Infrastructure for Optimization of a Massive Parallel Sequencing Workflow

Massive Parallel Sequencing is a term used to describe several revolutionary approaches to DNA sequencing, the so-called Next Generation Sequencing technologies. These technologies generate millions of short sequence fragments in a single run and can be used to measure levels of gene expression and to identify novel splice variants of genes allowing more accurate analysis. The proposed solution provides novelty on two fields, firstly an optimization of the read mapping algorithm has been designed, in order to parallelize processes, secondly an implementation of an architecture that consists of a Grid platform, composed of physical nodes, a Virtual platform, composed of virtual nodes set up on demand, and a scheduler that allows to integrate the two platforms.
Word Alignment and the Expectation-Maximization Algorithm

The purpose of this tutorial is to give you an example of how to take a simple discrete probabilistic model and derive the expectation maximization updates for it and then turn them into code. We give some examples and identify the key ideas that make the algorithms work. These are meant to be as intuitive as possible, but for those curious about the underlying mathematics, we also provide some derivations, and point the reader to additional tutorials when mathematical depth goes beyond the scope of the tutorial. But you needn’t follow these derivations in detail to understand the main concepts, so whether you try to is to is up to you. We’ll consider IBM Model 1, which has been the subject of other similar tutorials [Collins(2011), Knight(1999)]. A great deal of the material here is inspired, either directly or indirectly, by these papers, as well as the concise original description in Sections 4 and 4.1 of [Brown et al.(1993)], and of course, Chapter 4 of Philipp Koehn’s textbook (2010) . Everyone learns differently, though, so I urge you to take a look at one of those if you’re still confused after reading this one. The basic plan is to learn a conditional probabilistic model of a French sentence f given an English sentence e, which we’ll denote pθ(f|e). Subscript θ refers to the set of parameters in the model; we’ll describe what it looks like shortly. We also have a dataset D of N sentence pairs that are known to be translations of each other, D = {(f(1), e(1)). (f(N), e(N))}, where each superscript (n) indexes a different sentence pair. The object of our model will be to uncover the hidden word-to-word correspondences in these translation pairs. We will learn the model from this data, and once we’ve learned it, we’ll use it to predict the existence of the missing word alignments.
Output-based objective measure for non-intrusive speech quality evaluation

: - This paper describes a newly developed output-based method for non-intrusive evaluation of speech quality of voice communication systems, and evaluates its performance. The method, which uses only the output of the system, is based on measuring perceptually motivated objective auditory distances between the voiced parts of the speech signal whose quality to be evaluated to appropriately matching reference vectors extracted from a pre-formulated codebook. The codebook is formed by optimally clustering large number of perceptually-based parametric vectors extracted from a database of clean speech signals. The auditory distance measures are then mapped into equivalent subjective score, represented by the Mean Opinion scores (MOS), using regression. The required clustering and matching processes are achieved by using an efficient neural network based data mining technique known as the Self-Organizing Map. Perceptual, speaker-independent parametric representation of the speech is achieved by using Linear Prediction (PLP) and Bark Spectrum analysis. Reported evaluation results show that the proposed system is robust against speaker, utterance and distortion variations, and outperforms the ITU-T P.862 Perceptual Evaluation of Speech Quality (PESQ) for cases of speech degraded by channel impairments.
BOXTREE: A Hierarchical Representation for Surfaces in 3D

We introduce the boxtree, a versatile data structure for representing triangulated or meshed surfaces in 3D. A boxtree is a hierarchical structure of nested boxes that supports efficient ray tracing and collision detection. It is simple and robust, and requires minimal space. In situations where storage is at a premium, boxtrees are effective alternatives to octrees and BSP trees. They are also more flexible and efficient than R‐trees, and nearly as simple to implement.
Understanding the Remodelling of Cell Walls during Brachypodium distachyon Grain Development through a Sub-Cellular Quantitative Proteomic Approach

Brachypodium distachyon is a suitable plant model for studying temperate cereal crops, such as wheat, barley or rice, and helpful in the study of the grain cell wall. Indeed, the most abundant hemicelluloses that are in the B. distachyon cell wall of grain are (1-3)(1-4)-β-glucans and arabinoxylans, in a ratio similar to those of cereals such as barley or oat. Conversely, these cell walls contain few pectins and xyloglucans. Cell walls play an important role in grain physiology. The modifications of cell wall polysaccharides that occur during grain development and filling are key in the determination of the size and weight of the cereal grains. The mechanisms required for cell wall assembly and remodelling are poorly understood, especially in cereals. To provide a better understanding of these processes, we purified the cell wall at three developmental stages of the B. distachyon grain. The proteins were then extracted, and a quantitative and comparative LC-MS/MS analysis was performed to investigate the protein profile changes during grain development. Over 466 cell wall proteins (CWPs) were identified and classified according to their predicted functions. This work highlights the different proteome profiles that we could relate to the main phases of grain development and to the reorganization of cell wall polysaccharides that occurs during these different developmental stages. These results provide a good springboard to pursue functional validation to better understand the role of CWPs in the assembly and remodelling of the grain cell wall of cereals.
An Active Learning Approach for Embodied Concept Learning

In many natural settings training data is gained interactively, by taking actions, making queries, or performing experiments. In such a situation agent’s most powerful tool is its ability to act, to gather data, and to influence the world it is trying to understand [1]. Most of research on active learning relies on black-box approach by assuming free-of-cost availability of required training data through a query, e.g., [13], [14]. An embodied agent, however, has to consider many aspects such as sensori-motor capabilities, ability to design experiments to generate required training examples, cost of such interventional experiments, and so on. Such characteristics of real world demand a careful analysis of otherwise theoretically near-optimal active learning algorithms. Research on active learning for embodied agent has recently received more attention. Most of this work deals with sensorymotor learning such as learning forward model [2], [3], [4] or autonomous exploration, e.g., importance resampling for simultaneous localization and mapping (SLAM)[5]. On a higher level of abstraction, several discovery systems have exploited active learning for improving domain knowledge for planning [6], [7], [8]. Except a recent experimentation strategy for learning qualitative models through sensory motor exploration [9] , there is hardly any research work in field of active concept learning by an embodied agent. To address open ended concept leanring by an autonomous robot, EU project XPERO 1 aims to develop an embodied cognitive system, which is able to conduct experiments in the real world with the purpose of gaining new knowledge about the world and to develop and improve its own cognitive skills and overall performance. XPERO provides an ideal environment for robotic discovery by defining several real world scenarios. The underlying learning challenges, for the robot, range from learning ego-motion from sensorimotor data to learning high level concepts such as movability of lightweight boxes as result of its action. The robot not only learns qualitative and quantitative laws but also gains new insight which enables it to simplify its theory about environment [10], [11], [9]. Gaining insight, requires that learned concepts be interpretable this rules out many otherwise established
Are Networks Priced? Network Topology and Order Trading Strategies in High Liquidity Markets

Network spillovers explain as much as 90% of the individual variation in returns in a fully electronic market. We study two fully electronic, highly liquid markets, the Dow an SP this topology allows us to identify precisely both the relevance of network structure as well as endogenous network spillovers. Within these markets, we will show that network positioning on the part of trader leads to remarkable spillovers in return. Empirically, we estimate that the implied average multiplier, the ratio of a individual level shock to the total network one, is as large as 20. A gain of $1 for a trader leads to an average of $20 in gains for all traders and much more for connected ones. In a zero-sum market, such as the one in this study, this suggests a very large re-allocation of returns according to network structure.
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning

Genetic algorithms (GAs) are biologically motivated adaptive systems which have been used, with varying degrees of success, for function optimization. In this study, an abstraction of the basic genetic algorithm, the Equilibrium Genetic Algorithm (EGA), and the GA in turn, are reconsidered within the framework of competitive learning. This new perspective reveals a number of different possibilities for performance improvements. This paper explores population-based incremental learning (PBIL), a method of combining the mechanisms of a generational genetic algorithm with simple competitive learning. The combination of these two methods reveals a tool which is far simpler than a GA, and which out-performs a GA on large set of optimization problems in terms of both speed and accuracy. This paper presents an empirical analysis of where the proposed technique will outperform genetic algorithms, and describes a class of problems in which a genetic algorithm may be able to perform better. Extensions to this algorithm are discussed and analyzed. PBIL and extensions are compared with a standard GA on twelve problems, including standard numerical optimization functions, traditional GA test suite problems, and NP-Complete problems.
A User centric design Approach for Mobile Information and Entertainment Services (MIES) Implemented

The fast developing mobile technology and the lack of practices makes it difficult to design mobile information and entertainment services (MIES) with a satisfying quality within allocated time and budget. In this paper, the challenge is taken up by following a user centric design approach. A Service Design Approach is proposed that consists of activities and tools and in which users are involved during each phase. For example, a Group Support System for acquiring user requirements and a user interface design laboratory for testing the navigation structure. A Consent Provider Model is used to deal with privacy issues. The whole approach is illustrated by an action research project in which MIES were developed on a UMTS testbed and used by visitors at a university campus.
Hierarchical Dialogue Act Classification in Online Tutoring Sessions

As the corpora of online tutoring sessions grow by orders of magnitude, dialogue act classification can be used to capture increasingly fine-grained details about events during tutoring. In this paper, we apply machine learning to build models that can classify 133 (126 defined acts plus 7 to represent unknown and undefined acts) possible dialogue acts in tutorial dialog from online tutoring services. We use a data set of approximately 95000 annotated utterances to train and test our models. Each model was trained to predict top level Dialogue Acts using several learning algorithms. The best learning algorithm from top level Dialogue Acts was then applied to learn subcategories which was then applied in multi-level classification.
Data Fusion Obtained from Multiple Images Aiming the Navigation of Autonomous Intelligent Vehicles in Agricultural Environment

Visual navigation is an important research field in robotics due to low cost of cameras and the good results that these systems usually achieve. This paper presents monocular and stereo vision-based detection methods. The obstacles are detected and fused through the Dempster-Shafer theory for generating a cloud of points that contains the probability of the existence of obstacles in the environment and its distance from the autonomous vehicle. The experiments were performed in a real rural environment to evaluate and validate the approach. The proposed system has shown to be a promising approach for obstacle detection aimed at navigating an autonomous vehicle in rural and agricultural environments.
Leadership and innovation: The moderator role of organization support for innovative behaviors

Abstract This study aims to advance the understanding of direct and moderated effect of transformational leadership on organizational innovation. A new framework is proposed in this research to identify the moderating effect of organization support for idea generating, risk taking and decision-making on the relationship between transformational leadership and organizational innovation. A sample of 63 companies from top 100 Iranian companies participated in this research. The results of this study supported the expected positive relationship between transformational leadership and organizational innovation. Besides, two of the predicted moderating effects were supported in this research. The results suggest that transformational leaders might not only promote innovative activity within the organization but also ensure the market success of the innovations.
Impact of Health System Inputs on Health Outcome: A Multilevel Longitudinal Analysis of Botswana National Antiretroviral Program (2002-2013)

Objective To measure the association between the number of doctors, nurses and hospital beds per 10,000 people and individual HIV-infected patient outcomes in Botswana. Design Analysis of routinely collected longitudinal data from 97,627 patients who received ART through the Botswana National HIV/AIDS Treatment Program across all 24 health districts from 2002 to 2013. Doctors, nurses, and hospital bed density data at district-level were collected from various sources. Methods A multilevel, longitudinal analysis method was used to analyze the data at both patient- and district-level simultaneously to measure the impact of the health system input at district-level on probability of death or loss-to-follow-up (LTFU) at the individual level. A marginal structural model was used to account for LTFU over time. Results Increasing doctor density from one doctor to two doctors per 10,000 population decreased the predicted probability of death for each patient by 27%. Nurse density changes from 20 nurses to 25 nurses decreased the predicted probability of death by 28%. Nine percent decrease was noted in predicted mortality of an individual in the Masa program for every five hospital bed density increase. Conclusion Considerable variation was observed in doctors, nurses, and hospital bed density across health districts. Predictive margins of mortality and LTFU were inversely correlated with doctor, nurse and hospital bed density. The doctor density had much greater impact than nurse or bed density on mortality or LTFU of individual patients. While long-term investment in training more healthcare professionals should be made, redistribution of available doctors and nurses can be a feasible solution in the short term.
A Framework for Multifaceted Evaluation of Student Models

Latent variable models, such as the popular Knowledge Tracing method, are often used to enable adaptive tutoring systems to personalize education. However, finding optimal model parameters is usually a difficult non-convex optimization problem when considering latent variable models. Prior work has reported that latent variable models obtained from educational data vary in their predictive performance, plausibility, and consistency. Unfortunately, there are still no unified quantitative measurements of these properties. This paper suggests a general unified framework (that we call Polygon) for multifaceted evaluation of student models. The framework takes all three dimensions mentioned above into consideration and offers novel metrics for the quantitative comparison of different student models. These properties affect the effectiveness of the tutoring experience in a way that traditional predictive performance metrics fall short. The present work demonstrates our methodology of comparing Knowledge Tracing with a recent model called FeatureAware Student Knowledge Tracing (FAST) on datasets from different tutoring systems. Our analysis suggests that FAST generally improves on Knowledge Tracing along all dimensions studied.