Miracle-dz/UniFAR-FTUs huggingface dataset:
A Random Walk Approach to Selectional Preferences Based on Preference Ranking and Propagation

This paper presents an unsupervised random walk approach to alleviate data sparsity for selectional preferences. Based on the measure of preferences between predicates and arguments, the model aggregates all the transitions from a given predicate to its nearby predicates, and propagates their argument preferences as the given predicate’s smoothed preferences. Experimental results show that this approach outperforms several state-of-the-art methods on the pseudo-disambiguation task, and it better correlates with human plausibility judgements.
A computational analysis for boundary layer flow of magneto hydrodynamic tangent hyperbolic fluid of heat and mass transfer past a stretching cylinder with suction/injection using spectral relaxation method

Received: 15 September 2018 Accepted: 16 January 2019 The present paper intends to investigate and highlight the steady, two dimensional flow of heat and mass transfer of magneto hydrodynamic tangent hyperbolic fluid with suction/injection. In the present study tangent hyperbolic fluid is considered as the working fluid to investigate. The obvious viscosity of the study is varies to the extreme points i.e. between zero shear rates to the shear rate of infinity. Due to the stretching flow is induced. To get the numerical solution the powerful numerical technique that is spectral relaxation method is applied for the set of transformed equations those are derived from physical model of the flow. Thereafter, numerical outcomes are computed to discuss the convergence and accuracy of the proposed technique. The impacts of different flow controlling parameters which are experienced in the problem are resolved. All the obtained outcomes from the above numerical procedure are displayed through graphs and tables to discuss various resulting parameters. The main notable observations are it is found that on increasing Power law index (n) causes considerable increase in the thickness of the fluid. Therefore, the velocity profiles are decreased. Further, on increasing power law index causes considerable increase in both thicknesses of thermal and concentration boundary layers.
Risk factors for progression of subclinical diabetic macular oedema

Backgroud/aims The purpose of this study was to determine the incidence of, and risk factors dictating, progression of subclinical macular oedema (SCME) to clinically significant macular oedema (CSME) in patients with diabetes. Methods This was a retrospective, observational case-controlled study at the Veterans Administration (VA) Boston Healthcare System. The study group included subjects with central subfield macular thickness (CSMT) of 200–300 μm, measured by optical coherence tomography, compared with a control group with foveal thickness of <200 μm, matched for age, sex and duration of diabetes. Main outcomes measured were progression to CSME by clinical examination. Results A total 124 eyes of 73 diabetic patients from the greater Boston area were included in this retrospective study. The study group comprised 52 eyes of 37 diabetic patients with SCME in one or both eyes. The control group included 72 eyes of 36 patients without macular oedema. Sixteen eyes of 13 subjects (35%) progressed to CSME in the study group, compared with six eyes of four subjects (11%) in the control group. Stepwise logistic regression analysis confirmed that prior history of CSME increased the risk of progression (OR 3.69, CI 1.10 to 12.31, p=0.03). Logistic regression analysis also depicted a 15% increase in odds of progression with each 10 μm increase in CSMT (OR 1.15, CI 1.03 to 1.28, p=0.01). Conclusions The results and statistical analyses of this retrospective study suggest that a significant number of patients with SCME ultimately progress to CSME compared with controls. A significant association with prior history of CSME, advancing age and graded increases in CSMT was found.
A Linked List-Based Algorithm for Blob Detection on Embedded Vision-Based Sensors

Blob detection is a common task in vision-based applications. Most existing algorithms are aimed at execution on general purpose computers; while very few can be adapted to the computing restrictions present in embedded platforms. This paper focuses on the design of an algorithm capable of real-time blob detection that minimizes system memory consumption. The proposed algorithm detects objects in one image scan; it is based on a linked-list data structure tree used to label blobs depending on their shape and node information. An example application showing the results of a blob detection co-processor has been built on a low-powered field programmable gate array hardware as a step towards developing a smart video surveillance system. The detection method is intended for general purpose application. As such, several test cases focused on character recognition are also examined. The results obtained present a fair trade-off between accuracy and memory requirements; and prove the validity of the proposed approach for real-time implementation on resource-constrained computing platforms.
A 18.85 mW 20–29 GHz wideband CMOS LNA with 3.85±0.25 dB NF and 18.1±1.9 dB gain

A 20–29 GHz wideband CMOS low-noise amplifier (LNA) with flat and low noise figure (NF), flat and high gain (S21), and excellent phase linearity property (group-delay-variation is only ±22.6 ps across the whole band) is demonstrated. To achieve flat and low NF, the size, layout and bias of the input transistor were first optimized for minimum NF, and then the inductance of the input inductors was tuned to obtain a slightly under-damped (flat) NF frequency response. In addition, to achieve flat and high S21 and small group-delay-variation, the inductive-peaking technique was adopted in the current-reused stage for bandwidth enhancement. The LNA consumed 18.85 mW power and achieved flat and low NF of 3.85±0.25 dB, and flat and high S21 of 18.1±1.9 dB over the 20–29 GHz band of interest. These are the best NF and S21 performances ever reported for a 21.65–26.65 GHz or a 22–29 GHz wideband CMOS LNA.
Regulation of the Pyruvate Dehydrogenase Complexes in Plants a

Plants and animals have many metabolic processes in common. Convergent evolution has resulted in striking similarities in metabolic regulation. Superimposed upon the similarities, however, are differences arising from characteristic organismal requirements. The pyruvate dehydrogenase complex (PDC) in plant cells is illustrative of both the similarities in metabolic processes and the differences dictated by the need to respond to diverse external stimuli. Plants are unique in containing two distinct, spatially separated types of PDCs, one within the mitochondria1 matrix and the other in the plastid stroma. Each type of PDC has characteristic structural, catalytic, and regulatory properties. ' The mitochondrial location is typical for the PDC in eucaryotic organisms, where it serves as a primary entry point for carbon into the citric acid cycle. The plastid PDC provides both acetyl-CoA and NADH for fatty acid and isoprenoid biosynthesis. Thus, the first mechanism, and one of the most important, for regulation of plant PDCs is by compartmentation. In contrast to mammalian cells and microbes, all de novo fatty acid biosynthesis by plant cells occurs within the plastids.2 This is true for the chloroplasts in light-grown leaves, the chromoplasts in fruits and floral tissues, and the leucoplasts and amyloplasts in seeds and other non-green tissues. Plant cells also contain two glycolytic pathways, the classical cytoplasmic pathway and a second metabolic sequence within the p l a ~ t i d s . ~ It was a search for the connecting link between the pyruvate produced by plastid glycolysis and the acetyl-CoA necessary for fatty acid synthesis which led Dennis and his associates to discovery of the PDC within the leucoplasts of developing castor oil seed^. ^*^ Observations concerning the occurrence of plastid PDC were subsequently extended to green tissue^. ^. ' The chloroplast PDC not only produces acetyl-CoA for fatty acid biosynthesis, but also is the only known source to date for NADH in the chloroplast. '
Behavior Management by Walking Around.

In the best seller, In Search of Excellence, Peters and Waterman (1982) introduced Management by Walking Around (MBWA) as an organizational philosophy for corporate executives. HewlettPackard advocated Management by Wandering Around as a major aspect of the HP Way. Getting managers out of their corporate offices was promoted as a leadership style intended for managers to connect with, communicate with, and relate to all levels of employees. Peters and Waterman went on to suggest that excellent companies were significantly different from less excellent companies, because they promoted such interactive, informal communication networks within their organizations.
Adventure Author: a learning environment to support creative design

Creative design tasks are both challenging and rewarding for learners. While it is exciting to design a new product of which one can be proud, there can be many frustrations along the way. This paper considers the challenges faced by young people as they create their own computer games. It describes an initial model of the creative process involved in game design and presents qualitative analysis of interview data from two field studies focusing on the source and evolution of young people's ideas. The theoretically informed model of the creative process, and the empirical results are used to inform the design of software scaffolding for an interactive learning environment called Adventure Author.
COBrA – A Concept Ontology Browser for Anatomy

In developmental biology ontologies are used amongst other things to describe the anatomy of different species in meaningful ways. These ontologies developed independently for different species. An interesting task is now to establish homology links across these different species to use the knowledge of one organism for other ones. In this MSc project I developed and implemented an application that displays two ontologies simultaneously and enables biologists to make homology links across species. Two different visualisation techniques have been developed and implemented to evaluate how different perspectives of the ontology can help in exploring ontologies and support the biologist in making homology links. An evaluation has shown the potential of the different visualisations of the application and the functionality in achieving its supposed purpose.
Individual plasmacytoid dendritic cells are major contributors to the production of multiple innate cytokines in an organ-specific manner during viral infection

Abstract Plasmacytoid dendritic cells (pDCs) are an important source of IFN-α/β in response to a variety of viruses in vivo, including murine cytomegalovirus (MCMV). However, the respective contributions of various infected organs, and within these of pDCs, conventional dendritic cells and other cells, to the systemic production of IFN-α/β or other innate cytokines during viral infections in vivo is largely unknown. Whether a specialization of pDC subsets in the production of different patterns of innate cytokines exists in vivo in response to a viral infection has not been investigated. Here, by analyzing for the first time directly ex vivo, at the single-cell level, the simultaneous production of up to three cytokines in pDCs isolated from MCMV-infected mice, we show that (i) pDCs are the quasi-exclusive source of IFN-α/β, IL-12 and tumor necrosis factor (TNF)-α, early during MCMV infection, in two immunocompetent mouse lines and with two viral strains, (ii) pDC activation for IFN-α/β production is organ specific and (iii) a significant proportion of pDCs simultaneously produce IFN-α/β, TNF-α and IL-12, although TNF-α and IFN-α/β appear more often co-expressed with one another than each of them with IL-12. Altogether, these results show a broad and non-redundant role of pDCs in early innate detection of, and defense against, viral infection. The data also show differences in the responsiveness of pDCs from different tissues and suggest distinct molecular requirements for pDC production of various cytokines. These observations must be taken into account when designing new antiviral vaccination strategies aimed at harnessing pDC responses.
Robust and efficient environment detection for adaptive speech enhancement in cochlear implants

Cochlear implant (CI) recipients require alternative signal processing for speech enhancement, since the quantities needed for intelligibility and quality improvement differ significantly when direct stimulation of the basilar membrane is employed for CIs. Here, a robust feature vector is proposed for environment classification in CI devices. The feature vector is directly computed from the output of the advanced combination encoder (ACE), which is a sound coding strategy commonly used in CIs. Performance of the proposed feature vector is evaluated in the context of environment classification tasks under anechoic quiet, noisy, reverberant, and noisy reverberant conditions. Speech material taken from the IEEE corpus are used to simulate different environmental acoustic conditions with: 1) three measured room impulse responses (RIR) with distinct reverberation times (T60) for generating reverberant environments, and 2) car, train, white Gaussian, multi-talker babble, and speech-shaped noise (SSN) samples for creating noisy conditions at 4 different signal-to-noise ratio (SNR) levels. We investigate 3 different classifiers for environment detection, namely Gaussian mixture models (GMM), support vector machines (SVM), and neural networks (NN). Experimental results illustrate the effectiveness of the proposed features for environment classification.
Towards Optimal Active Learning for Matrix Factorization in Recommender Systems

Recommender systems help web users to address information overload. However their performance depends on the number of provided ratings by users. This problem is amplified for a new user because he/she has not provided any rating. To address this problem, active learning methods have been proposed to acquire those ratings from users, that will help most in determining their interests. The optimal active learning selects a query that directly optimizes the expected error for the test data. This approach is applicable for prediction models in which this question can be answered in closed-form given the distribution of test data is known. Unfortunately, there are many tasks and models for which the optimal selection cannot efficiently be found in closed-form. Therefore, most of the active learning methods optimize different, non-optimal criteria, such as uncertainty. Nevertheless, in this paper we exploit the characteristics of matrix factorization, which leads to a closed-form solution and by being inspired from existing optimal active learning for the regression task, develop a method that approximates the optimal solution for recommender systems. Our results demonstrate that the proposed method improves the prediction accuracy of MF.
Smoking cessation.

Cessation of cigarette smoking is the single most important therapeutic intervention that is effective in reducing the symptoms of COPD and in preventing its onset. Smoking cessation is, therefore, a major goal in efforts to mitigate the burden of this disease. This review will consider the pharmacologic and behavioral therapies that have been used to assist smokers in overcoming their addiction. These strategies assist a significant minority of smokers to stop smoking and, thus, they can have an important positive impact on COPD as well as on other health outcomes.
Minimizing Fuel Use During Power Transients for Naturally Aspirated and Turbo Charged Diesel Engines

Recent development has renewed the interest in drivetrain concepts which gives a higher degree of freedom by disconnecting the engine and vehicle speeds. This freedom raises the demand for active control, which especially during transients is not trivial, but of which the quality is crucial for the success of the drivetrain concept. In this work the fuel optimal engine operating point trajectories for a naturally aspirated and a turbocharged diesel engine, connected to a load which does not restrict the engine speed, is derived, analysed and utilized for nding a suboptimal operating point trajectory. The analysis and optimization is made with dynamic programming, Pontryagin’s maximum principle and a suboptimal strategy based on the static optimal operating points. Methods are derived for using Pontryagin’s maximum principle for nding the optimal operating point trajectories, for simple load cases. The time needed for computation is reduced a factor 1000 100, depending on engine layout, compared to dynamic programming. These methods are only applicable to very simple load cases though. Finally, a suboptimal calculation method which reduce the time needed for computation a factor > 1000 compared to dynamic programming, while showing a < 5% increase in fuel consumption compared to the optimal, is presented.
Intuitive application-specific user interfaces for mobile devices

The user interfaces on current-day mobile devices are limited owing to their small form factor. In this paper, we propose intuitive user interfaces for two applications on mobile phones with built-in cameras, namely, a car-racing game and a map navigation application. These user interfaces are based on the visual detection of the device's self-motion. We discuss three different methodologies to implement same and conduct user studies to analyze people's acceptance of the interfaces in comparison to controlling the applications with keys on the phone. Results show that our proposed interfaces are well appreciated for their intuitiveness, even though there is a learning curve.
The frequency and extent of sub-ice phytoplankton blooms in the Arctic Ocean

Recent thinning and ponding of Arctic sea ice may have led to frequent, extensive phytoplankton blooms under sea ice. In July 2011, the observation of a massive phytoplankton bloom underneath a sea ice–covered region of the Chukchi Sea shifted the scientific consensus that regions of the Arctic Ocean covered by sea ice were inhospitable to photosynthetic life. Although the impact of widespread phytoplankton blooms under sea ice on Arctic Ocean ecology and carbon fixation is potentially marked, the prevalence of these events in the modern Arctic and in the recent past is, to date, unknown. We investigate the timing, frequency, and evolution of these events over the past 30 years. Although sea ice strongly attenuates solar radiation, it has thinned significantly over the past 30 years. The thinner summertime Arctic sea ice is increasingly covered in melt ponds, which permit more light penetration than bare or snow-covered ice. Our model results indicate that the recent thinning of Arctic sea ice is the main cause of a marked increase in the prevalence of light conditions conducive to sub-ice blooms. We find that as little as 20 years ago, the conditions required for sub-ice blooms may have been uncommon, but their frequency has increased to the point that nearly 30% of the ice-covered Arctic Ocean in July permits sub-ice blooms. Recent climate change may have markedly altered the ecology of the Arctic Ocean.
ALGORAND: The Efficient and Democratic Ledger

Algorand is a truly decentralized, new, and secure way to manage a shared ledger. Unlike prior approaches based on {\em proof of work}, it requires a negligible amount of computation, and generates a transaction history that does not fork with overwhelmingly high probability. This approach cryptographically selects ---in a way that is provably immune from manipulations, unpredictable until the last minute, but ultimately universally clear--- a set of verifiers in charge of constructing a block of valid transactions. This approach applies to any way of implementing a shared ledger via a tamper-proof sequence of blocks, including traditional blockchains. This paper also presents more efficient alternatives to blockchains, which may be of independent interest. Algorand significantly enhances all applications based on a public ledger: payments, smart contracts, stock settlement, etc. But, for concreteness, we shall describe it only as a money platform.
Effects of Chronic Exercise on Measures of Body Shame in Obese College-Aged Individuals

The purpose of this investigation was to determine the effects of a 6-week structured exercise program on levels of body shame in obese college-aged individuals. It was hypothesized that participants would display lower levels of body-shame, higher levels of body-surveillance and body-control following the exercise intervention. It was also hypothesized that participants would have higher levels of self-determination upon completion of the program. Through a mixed methodology of open-ended, in-depth interviews and surveys it was determined self-conscious emotions decrease following a 6-week exercise program and participants display improved shame-coping mechanisms. iii 1 CHAPTER 1 – INTRODUCTION Obesity is a major public health issue in Western culture, especially within the United States. While the physiological illnesses associated with obesity (e.g., type II diabetes, coronary artery disease, hypertension, etc.) are significant they often receive more attention than the psychosocial issues (e.g., decreased self-esteem, increased body dissatisfaction and depression) that often plague obese individuals. Stigmatization of obese individuals within the United States is rampant, socially acceptable and can have severe effects on the mental health of those stigmatized Weight-based stigma is defined as prejudiced attitudes toward overweight or obese individuals (Myers & Rosen, 1999). Weight stigmatization often consists of attributing negative traits to an individual purely on the basis of his or her weight. Obese individuals report feeling stigmatized in many aspects of their lives, including professional, healthcare and educational settings providers, even those who specialize in the treatment of obesity, often hold negative stereotypes of obese individuals (Teachman & Brownell, 2001). Studies show children as young as 3 years old demonstrate negative attitudes toward obese children (Brylinsky & Moore, 1994). Obese individuals have also been shown to stigmatize other obese individuals, despite being obese themselves Teachman & Brownell, 2001). Common examples of weight stigmatization include 2 job discrimination, being stared at in public, being excluded or avoided by others or loved ones being embarrassed to be seen with them due to their weight. Obese individuals often experience weight stigma through the comments of loved ones and strangers alike (Friedman et al., 2005; Myers & Rosen, 1999). These comments may include unsolicited diet suggestions, assumptions that the individual lacks self-control due to their weight or cruel remarks regarding their size (Friedman et al., 2005; Myers & Rosen, 1999). Stigmatizing experiences due to obesity can elicit significant psychological distress (Myers & Rosen, 1999). Obese adolescents report higher levels of shame and have a higher …
Comparison of Influence of Two Data-Encoding Methods for Grover Algorithm on Quantum Costs

It is important to be able to calculate realistic estimates of quantum costs for real oracles used in quantum algorithms. In this paper, we compare Perkowski's[1] oracle data encoding method with Hogg's[2] encoding method for Grover algorithm[3], to examine the decrease in Oracle gate cost, if any, for four common constraint satisfaction problems: Graph coloring, Satisfiability, Send-More-Money and Max Clique.