Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic reasoner selection using machine learning

The ability to draw logical conclusions in ontologies from explicitly given axioms and facts is one of the key advantages of using semantic technologies. Based on the W3C recommendation of the Web Ontology Language (OWL) a variety of reasoners have been developed for this task. Different language profiles, reasoning algorithms, and special-purpose optimisation techniques have brought up reasoners with various strengths and weaknesses. Selecting the most suitable reasoner for a given reasoning scenario thus is a challenge. This paper presents an automatic reasoner selection approach based on machine learning techniques. The most important ontology and query features are identified and used for learning a model that can be used to predict the best performing reasoner for a given request. The approach is implemented as a strategy in a reasoning broker framework called HERAKLES. Using a training set consisting of 187 real-world ontologies found on the Internet, we evaluated four different machine learning techniques. The results show that a machine learning based reasoner selection strategy can predict the best performing reasoner for a given reasoning request with more than 77% accuracy.
Step-indexed models of call-byname : a tutorial example

In this tutorial paper we show how to construct a step-indexed logical relation for a call-by-name programming language with recursive types and show that it is complete with respect to contextual equivalence. We then show how the same constructions can be used to define a step-indexed model, in the standard categorical sense, of the language. We hope that this will make step-indexed techniques more readily available for researchers interested in call-by-name or call-by-need based languages (such as Haskell) and make it clear that step-indexed models can indeed be seen as (operationally-based) models in the technical sense (and not only as a reasoning technique for reasoning about contextual equivalence).
Protective Effects of Human iPS-Derived Retinal Pigment Epithelium Cell Transplantation in the Retinal Dystrophic Rat

Transformation of somatic cells with a set of embryonic transcription factors produces cells with the pluripotent properties of embryonic stem cells (ESCs). These induced pluripotent stem (iPS) cells have the potential to differentiate into any cell type, making them a potential source from which to produce cells as a therapeutic platform for the treatment of a wide range of diseases. In many forms of human retinal disease, including age-related macular degeneration (AMD), the underlying pathogenesis resides within the support cells of the retina, the retinal pigment epithelium (RPE). As a monolayer of cells critical to photoreceptor function and survival, the RPE is an ideally accessible target for cellular therapy. Here we report the differentiation of human iPS cells into RPE. We found that differentiated iPS-RPE cells were morphologically similar to, and expressed numerous markers of developing and mature RPE cells. iPS-RPE are capable of phagocytosing photoreceptor material, in vitro and in vivo following transplantation into the Royal College of Surgeons (RCS) dystrophic rat. Our results demonstrate that iPS cells can be differentiated into functional iPS-RPE and that transplantation of these cells can facilitate the short-term maintenance of photoreceptors through phagocytosis of photoreceptor outer segments. Long-term visual function is maintained in this model of retinal disease even though the xenografted cells are eventually lost, suggesting a secondary protective host cellular response. These findings have identified an alternative source of replacement tissue for use in human retinal cellular therapies, and provide a new in vitro cellular model system in which to study RPE diseases affecting human patients.
CS 5220: Project 2 - Smoothed Particle Hydrodynamics

We improve upon a baseline smoothed-particle hydrodynamics algorithm to bring it down from O(n2) time to O(n) time for n particles. We do so through a number of optimizations, which allow us to achieve up to a 67x speedup for large particle sizes over the baseline serial implementation. From this, we construct a parallel version of the code using OpenMP. We can achieve a 3.8x speedup over our optimized serial version when executing 8 threads. Furthermore, we show linear speedups as both the number of threads and the number of particles increase.
Haemophilus influenzae Porin Contributes to Signaling of the Inflammatory Cascade in Rat Brain

ABSTRACT In the present study we observed that the Haemophilus influenzae type b (Hib) porin, among the different surface bacterial components, is involved in the pathophysiology of bacterial meningitis. This study demonstrates that inoculation of Hib porin into the fourth cerebral ventricle causes the simultaneous expression of interleukin-1α (IL-1α), tumor necrosis factor alpha (TNF-α), and macrophage inflammatory protein 2 (MIP-2) at 6 h after inoculation. At 24 h, the expression of MIP-2 decreases while the expression of IL-1α and TNF-α increases. The mRNA expression of IL-1α, TNF-α, and MIP-2 is correlated with injury to the blood-brain barrier as demonstrated by the appearance of serum proteins and leukocytes in cerebrospinal fluid and by the increase in brain water content.
Dynamic and memory efficient web page prediction model using LZ78 and LZW algorithms

Web access prediction has attracted significant attention in recent years. Web prefetching and some personalization systems use prediction algorithms. Most current applications that predict the next user web page have an offline component that does the data preparation task and an online section that provides personalized content to the users based on their current navigational activities. In this paper we present an online prediction model that does not have an offline component and fit in the memory with good prediction accuracy. Our algorithm is based on LZ78 and LZW algorithms that are adapted for modeling the user navigation in web. Our model decreases computational complexities which is a serious problem in developing online prediction systems. A performance evaluation is presented using real web logs. This evaluation shows that our model needs much less memory than PPM family of algorithms with good prediction accuracy.
Trends in under-5 mortality rates and the HIV/AIDS epidemic.

INTRODUCTION The prevalence of human immunodeficiency virus (HIV) among adults and mortality rates among under-5-year-olds have increased or stagnated in many countries. The objective of this study was to investigate whether there is a link between under-5 mortality trends and the prevalence of HIV among adults and, if so, to assess the magnitude of the effect of adult HIV prevalence on under-5 mortality rates. METHOD Data from Demographic and Health Surveys were used to establish the trends in under-5 mortality rates for 25 countries for which there are data for at least two points in time. Countries were ranked according to the most recent adult HIV prevalence data and grouped in three categories: those with very high HIV prevalence (> or = 5%); those with moderately high prevalence (1-4.9%); and those with low prevalence (< 1%). A mathematical model was fitted to obtain an estimate of the contribution of HIV/AIDS to the level of under-5 mortality in each country. RESULTS Under-5 mortality rates showed an increase in most countries with high adult HIV prevalence, but a decrease in almost every country with moderately high or low prevalence. The estimated contribution of adult HIV prevalence to the observed level of under-5 mortality was highest (up to 61%) in Zimbabwe (where HIV prevalence was highest) and tended to decrease with the level of HIV prevalence. DISCUSSION The contribution of HIV/AIDS to childhood mortality therefore appears to be most noticeable in settings where the epidemic is most severe.
Pneumococcal conjugate vaccination for older adults

Hollingsworth and Isturiz1 have raised several questions about our earlier review of the use of pneumococcal conjugate (PCV) or polysaccharide (PPV) vaccine for older adults (i.e., ≥65 y of age). 2 They begin by citing two metaanalyses published in 2009 and 2013 that concluded that prevention of pneumococcal pneumonia could not be demonstrated for PPV.3,4 They overlook my earlier review of five metaanalyses that was published in 2004.5 This review showed that the study populations in prospective trials of PPV were often not representative of the populations of elderly and high-risk adults for whom PPV is recommended. Furthermore, the five metaanalyses often omitted clinical trials that should have been evaluated, included trials that should have been omitted, and frequently miscounted the numbers of subjects and outcome events in the individual clinical trials. More important, retrospective sample size calculations showed that none of the five metaanalyses included an adequate number of person years of observation to rule out false-negative results. The numbers speak for themselves and cannot be ignored. Simply put, metaanalyses of the PPV clinical trials will never tell us whether the vaccine prevents pneumococcal pneumonia or all-cause pneumonia in elderly and high-risk adults, and consequently they must be regarded as inconclusive and uninformative. Since 2004, only one small prospective clinical trial of PPV has been published (reviewed in 6). Nothing new has been added to our knowledge of PPV efficacy by the more recent metaanalyses.3,4 Epidemiologists have reminded us that the lack of evidence of PPV efficacy is not evidence of its absence. The conclusions of our earlier review still stand.5
Counting Packings of Generic Subsets in Finite Groups

A packing of subsets $\mathcal S_1,. , \mathcal S_n$ in a group $G$ is a sequence $(g_1,.,g_n)$ such that $g_1\mathcal S_1,. ,g_n\mathcal S_n$ are disjoint subsets of $G$. We give a formula for the number of packings if the group $G$ is finite and if the subsets $\mathcal S_1,. ,\mathcal S_n$ satisfy a genericity condition. This formula can be seen as a generalization of the falling factorials which encode the number of packings in the case where all the sets $\mathcal S_i$ are singletons.
Randomization and the American Put

While American calls on non-dividend paying stocks may be valued as European, there is no completely explicit exact solution for the values of American puts. We introduce a novel technique called randomization to value American puts and calls on dividend-paying stocks. This technique yields a new semi-explicit approximation for American option values in the Black Scholes model. Numerical results indicate that the approximation is both accurate and computationally efficient.
Artificial Neural Network Based Harmonic Optimization of Multilevel Inverter to Reduce THD

— A novel concept of application of Artificial Neural Networks (ANN) for estimating the optimum switching angles for the voltage and harmonic control of cascaded multilevel inverters is presented. In this paper, the neural network is trained off line using the desired switching angles given by the classic harmonic elimination strategy to any value of the modulation index. After training the proposed ANN system, a large and memory-demanding look-up table is replaced with trained neural network to generate the optimum switching angles with lowest Total Harmonic Distortion (THD) for a given modulation index. This technique can be applied to multilevel inverters with any number of levels. As an example, a seven-level and eleven-level inverter is considered and the optimum switching angles are calculated, in order to eliminate the odd harmonics and to reduce THD. The ANN control algorithm is to be implemented using m-file program. Theoretical concepts have been validated in simulation results using artificial neural network technique which shows the high performance and technical advantages of the developed system.
A Neural-Fuzzy Framework for Modeling Car-following Behavior

A general framework is introduced to model driver behavior from real car-following data acquired on Swedish roads using an advanced instrumented vehicle. In early research, the data was classified into different car-following regimes based on fuzzy clustering methods and knowledge obtained from video analysis. In this paper, we propose a multi-regime framework based on the statistical property in each regime and mathematical models adopted in those regimes. This framework is an extension of TSK fuzzy inference system and can be expressed by a neural-fuzzy system. Genetic algorithm (GA) is designed as the main learning method for this system. In practice, this model structure illustrates human knowledge of car-following in a more understandable manner and can be rather flexible as the regime parameters and model forms may vary according to the application context.
A 52-GHz 8.5-dB Traveling-Wave Amplifier in 0.13-$\mu$m Standard CMOS Process

The design of a traveling-wave amplifier (TWA) in 0.13-mum standard CMOS technology is presented. It is designed to maximize the gain-bandwidth product (GBP). An asymmetric cascode, coplanar waveguide (CPW), and loss compensation technique enables maximization of the TWA GBP. Design and modeling of 90-Omega CPW used to synthesize inductors of the TWA lines is presented. Simulations with a design kit and developed models for CPW show a 52-GHz bandwidth with a maximum power gain of 8.5 dB at 10 GHz for a 135-mW power consumption. Measurements up to 40 GHz confirm these results.
Portable speech interpreter which has voice input and sophisticated correction functions

This paper describes the development of a portable speech interpreter by which the user can look for words and relating sentences easily using his/her voice. The prototype uses a RISC microprocessor for the embedded use, resulting in a compact body of 15cm(W) and 6cm(H). The voice user interface is helpful, especially in small devices, but it has a serious problem related to the recognition errors. The total user interface must be designed carefully to minimize the inconvenience caused by the recognition errors. We have developed an interface in which the confinement of the dictionary realizes the quick and accurate recognition, and the syllable correction procedure provides an easy way to correct recognition errors. These features make it possible to input any words, which are found in a standard Japanese dictionary. Experimental evaluations show that the N-best recognition rate for words and syllables are acceptable.
Identification, summary and comparison of tools used to measure organizational attributes associated with chronic disease management within primary care settings

Rationale, aims and objectives Given the increasing emphasis being placed on managing patients with chronic diseases within primary care, there is a need to better understand which primary care organizational attributes affect the quality of care that patients with chronic diseases receive. This study aimed to identify, summarize and compare data collection tools that describe and measure organizational attributes used within the primary care setting worldwide. Methods Systematic search and review methodology consisting of a comprehensive and exhaustive search that is based on a broad question to identify the best available evidence was employed. Results A total of 30 organizational attribute data collection tools that have been used within the primary care setting were identified. The tools varied with respect to overall focus and level of organizational detail captured, theoretical foundations, administration and completion methods, types of questions asked, and the extent to which psychometric property testing had been performed. The tools utilized within the Quality and Costs of Primary Care in Europe study and the Canadian Primary Health Care Practice-Based Surveys were the most recently developed tools. Furthermore, of the 30 tools reviewed, the Canadian Primary Health Care Practice-Based Surveys collected the most information on organizational attributes. Conclusions There is a need to collect primary care organizational attribute information at a national level to better understand factors affecting the quality of chronic disease prevention and management across a given country. The data collection tools identified in this review can be used to establish data collection strategies to collect this important information.
Mental stress assessment based on feature level fusion of fNIRS and EEG signals

This study aims to improve the detection rate of mental stress using the complementary nature of functional Near Infrared Spectroscopy (fNIRS) and Electroencephalogram (EEG). Simultaneous measurements of fNIRS and EEG signals were conducted on 12 subjects while solving arithmetic problems under two different conditions (control and stress). The stressors in this work were time pressure and negative feedback of individual performance. The study demonstrated significant reduction in the concentration of oxygenated haemoglobin (p=0.0032) and alpha rhythm power (p=0.0213) on the prefrontal cortex (PFC) under stress condition. Specifically, the right PFC and dorsolateral PFC were highly sensitive to mental stress. Using support vector machine (SVM), the mean detection rate of mental stress was 91%, 95% and 98% using fNIRS, EEG and fusion of fNIRS and EEG signals, respectively.
Efficient adaptive designs with mid‐course sample size adjustment in clinical trials

Adaptive designs have been proposed for clinical trials in which the nuisance parameters or alternative of interest are unknown or likely to be misspecified before the trial. Although most previous works on adaptive designs and mid‐course sample size re‐estimation have focused on two‐stage or group‐sequential designs in the normal case, we consider here a new approach that involves at most three stages and is developed in the general framework of multiparameter exponential families. This approach not only maintains the prescribed type I error probability but also provides a simple but asymptotically efficient sequential test whose finite‐sample performance, measured in terms of the expected sample size and power functions, is shown to be comparable to the optimal sequential design, determined by dynamic programming, in the simplified normal mean case with known variance and prespecified alternative, and superior to the existing two‐stage designs and also to adaptive group‐sequential designs when the alternative or nuisance parameters are unknown or misspecified. Copyright © 2008 John Wiley & Sons, Ltd.
Quantitative Proteomics Reveals Cellular Targets of Celastrol

Celastrol, a natural substance isolated from plant extracts used in traditional Chinese medicine, has been extensively investigated as a possible drug for treatment of cancer, autoimmune diseases, and protein misfolding disorders. Although studies focusing on celastrol's effects in specific cellular pathways have revealed a considerable number of targets in a diverse array of in vitro models there is an essential need for investigations that can provide a global view of its effects. To assess cellular effects of celastrol and to identify target proteins as biomarkers for monitoring treatment regimes, we performed large-scale quantitative proteomics in cultured human lymphoblastoid cells, a cell type that can be readily prepared from human blood samples. Celastrol substantially modified the proteome composition and 158 of the close to 1800 proteins with robust quantitation showed at least a 1.5 fold change in protein levels. Up-regulated proteins play key roles in cytoprotection with a prominent group involved in quality control and processing of proteins traversing the endoplasmic reticulum. Increased levels of proteins essential for the cellular protection against oxidative stress including heme oxygenase 1, several peroxiredoxins and thioredoxins as well as proteins involved in the control of iron homeostasis were also observed. Specific analysis of the mitochondrial proteome strongly indicated that the mitochondrial association of certain antioxidant defense and apoptosis-regulating proteins increased in cells exposed to celastrol. Analysis of selected mRNA transcripts showed that celastrol activated several different stress response pathways and dose response studies furthermore showed that continuous exposure to sub-micromolar concentrations of celastrol is associated with reduced cellular viability and proliferation. The extensive catalog of regulated proteins presented here identifies numerous cellular effects of celastrol and constitutes a valuable biomarker tool for the development and monitoration of disease treatment strategies.
Simultaneous expression of H-2-restricted and alloreactive recognition by a cloned line of influenza virus-specific cytotoxic T lymphocytes

Based on theoretical considerations and several types of experimental evidence with heterogeneous cell populations it has been proposed that alloreactive T cells and major histocompatibility complex (MHC)- restricted T cells directed to foreign non-NHC antigens represent overlapping subsets. In this report we provide direct evidence for this hypothesis at the clonal level. We have isolated a cloned continuous influenza virus-specific cytotoxic T cell (CTL) line derived from a single (H-2b X H-2d)F1 CTL precursor which simultaneously exhibits H-2- restricted cytotoxicity of influenza A/Japan/305/57 virus in association with H-2Kd and alloreactive cytotoxicity for H-2Kk alloantigen. Cold target inhibition data demonstrate that both MHC- restricted and alloreactive recognition is mediated by the same cell population. In addition to cross-reactivity at the target cell level, we shown that this cloned CTL line can be specifically stimulated to proliferate either by A/Japan/305/57 virus-infected cells expressing H- 2Kd or by uninfected cells of the H-2Kk haplotype.
A vaccinia virus-vectored Hantaan virus vaccine protects hamsters from challenge with Hantaan and Seoul viruses but not Puumala virus

To investigate the ability of a vaccinia virus-vectored vaccine expressing the M and the S segments of Hantaan (HTN) virus (C. S. Schmaljohn, S. E. Hasty, and J. M. Dalrymple, Vaccine 10:10-13, 1992) to elicit a protective immune response against other hantaviruses, we vaccinated hamsters with the recombinant vaccine and challenged them with HTN, Seoul (SEO), or Puumala (PUU) virus. Neutralizing antibodies to HTN virus were found in all vaccinated hamsters both before and after challenge. Neutralizing antibody titers to SEO virus were present at low levels or were undetectable after two immunizations with the vaccine but were positive in all vaccinated hamsters after challenge with SEO virus and were also positive in control animals that were not challenged. Neutralizing antibodies to PUU virus were observed only in hamsters previously challenged with PUU virus. To assay for virus in the blood and tissues of the hamsters, we developed a nested reverse transcriptase (RT)-PCR with cross-reactive outer primers and serotype-specific inner primers. The RT-PCR specifically detected as little as 1 PFU of virus in serum containing high-titer neutralizing antibodies and was more sensitive than immunofluorescent antibody staining for detecting virus in lung and kidney specimens of infected hamsters. By using the RT-PCR, we found that vaccinated hamsters, challenged with HTN or SEO virus, neither were viremic nor had evidence of virus in their lungs or kidneys. In contrast, vaccinated hamsters challenged with PUU virus were viremic and had PUU virus-specific nucleic acid in their organs.