Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Critical Formula and Mathematical Model of Double-Tap Interphase Reactor in a Six-Phase Tap-Changer Diode Rectifier

This paper presents a new practical formula to calculate the critical value of interphase reactor (IPR) in a six-phase diode rectifier with tap-changer. In addition, a mathematical model of the double-tap IPR has been established by equivalent circuit method. Simulation and experimental results verify the validity of the formula and mathematical model. On the basis of the above achievements, a simple and effective approach is provided to design the IPR and to analyze the performance of the whole system
Determination of the minimum inhibitory concentration of Cryptococcus neoformans and Cryptococcus gattii against fluconazole by flow cytometry.

Recent studies have used flow cytometry (FCM) as an important alternative method to determine the antifungal susceptibility of yeasts compared to the broth microdilution Clinical and Laboratory Standards Institute (CLSI) reference procedure. We present a comparative study of the broth microdilution method and flow cytometry to assess the in vitro antifungal susceptibility of Cryptococcus neoformans (n = 16) and C. gattii (n = 24) to fluconazole. The minimum inhibitory concentration (MIC) assays by flow cytometry were defined as the lowest drug concentration that showed ∼50% of the count of acridine orange negative cells compared to that of the growth control. Categorical classification showed all C. neoformans isolates were susceptible to fluconazole. Three isolates of C. gattii were susceptible dose-dependent and the remaining 21 isolates were classified as susceptible. MICs comparison of both methodologies demonstrated 100% categorical agreement of the results obtained for C. neoformans and C. gattii. The MICs obtained with the CLSI-approved method and flow cytometry were compared by the Spearman correlation test and a significant Pv = 0.001. The flow cytometric method has the advantage of analyzing a large and constant number of cells in less time, i.e., 9 h incubation for fluconazole using acridine orange versus 72 h for broth microdilution method. In conclusion, the two methods were comparable and flow cytometry method can expedite and improve the results of in vitro susceptibility tests of C. neoformans and C. gattii against fluconazole and also allows comparative studies in vitro/in vivo more rapidly, which along with clinical data, could assist in selecting the most appropriate treatment choice.
Adaptive high gain observers for a class of nonlinear systems with nonlinear parametrization

In this paper, an adaptive observer is proposed for a class of uniformly observable nonlinear systems with nonlinear parametrization. The main novelty of the proposed observer is the introduction of characteristic indices, which allow a natural construction of the gain matrix. The main properties of the proposed observer are highlighted in simulation through an example dealing with the identification of an engine transient fuel characterized by a delay on the input.
Exploring Supervised LDA Models for Assigning Attributes to Adjective-Noun Phrases

This paper introduces an attribute selection task as a way to characterize the inherent meaning of property-denoting adjectives in adjective-noun phrases, such as e.g. hot in hot summer denoting the attribute temperature, rather than taste. We formulate this task in a vector space model that represents adjectives and nouns as vectors in a semantic space defined over possible attributes. The vectors incorporate latent semantic information obtained from two variants of LDA topic models. Our LDA models outperform previous approaches on a small set of 10 attributes with considerable gains on sparse representations, which highlights the strong smoothing power of LDA models. For the first time, we extend the attribute selection task to a new data set with more than 200 classes. We observe that large-scale attribute selection is a hard problem, but a subset of attributes performs robustly on the large scale as well. Again, the LDA models outperform the VSM baseline.
Opinion dynamics of skeptical agents

How does skepticism affect opinion formation in networks? In many settings, agents exhibit skepticism in the presence of people whose beliefs radically different from their own, and they are reluctant to be persuaded by such individuals. We present a model of opinion dynamics where agents are receptive toward other agents that have similar opinions, but remain skeptical of agents holding disparate opinions. We analyze how agents with extreme opinions affect the general population, using simulations on Barabiasi-Albert random graphs, and modified Erdos-Renyi random graphs that incorporate homophily. Finally, we show that even skeptical agents are able to come to an early consensus and take coordinated action to reach a final opinion in most settings; but, agents in homophilic networks may fail to converge to a single opinion. Paradoxically, this happens when agents are least skeptical, and are able to stabilize themselves by balancing influence from extremists from opposing camps.
Generalized gamma modeling of speech and its online estimation for speech enhancement

Generalized gamma modeling and its online method of parameter estimation of speech spectral magnitude are proposed for MAP based speech enhancement systems. Generalized gamma modeling is shown to be a natural extension of the Gaussian modeling of speech spectral component distribution, and is therefore, able to fit the prior distribution better than the conventional method. An online parameter estimation method for the gamma distribution, based on a moment matching method, is then proposed. The effectiveness of the proposed methods are confirmed by improvement in both SNR and ASR using the AURORA2 standard database, where about 4 dB improvement in SNR and 20% improvement in relative ASR performance are obtained.
Quantitative evaluation of automated skull‐stripping methods applied to contemporary and legacy images: Effects of diagnosis, bias correction, and slice location

Performance of automated methods to isolate brain from nonbrain tissues in magnetic resonance (MR) structural images may be influenced by MR signal inhomogeneities, type of MR image set, regional anatomy, and age and diagnosis of subjects studied. The present study compared the performance of four methods: Brain Extraction Tool (BET; Smith [ 2002 ]: Hum Brain Mapp 17:143–155); 3dIntracranial (Ward [ 1999 ] Milwaukee: Biophysics Research Institute, Medical College of Wisconsin; in AFNI); a Hybrid Watershed algorithm (HWA, Segonne et al. [ 2004 ] Neuroimage 22:1060–1075; in FreeSurfer); and Brain Surface Extractor (BSE, Sandor and Leahy [ 1997 ] IEEE Trans Med Imag 16:41–54; Shattuck et al. [ 2001 ] Neuroimage 13:856–876) to manually stripped images. The methods were applied to uncorrected and bias‐corrected datasets; Legacy and Contemporary T1‐weighted image sets; and four diagnostic groups (depressed, Alzheimer's, young and elderly control). To provide a criterion for outcome assessment, two experts manually stripped six sagittal sections for each dataset in locations where brain and nonbrain tissue are difficult to distinguish. Methods were compared on Jaccard similarity coefficients, Hausdorff distances, and an Expectation‐Maximization algorithm. Methods tended to perform better on contemporary datasets; bias correction did not significantly improve method performance. Mesial sections were most difficult for all methods. Although AD image sets were most difficult to strip, HWA and BSE were more robust across diagnostic groups compared with 3dIntracranial and BET. With respect to specificity, BSE tended to perform best across all groups, whereas HWA was more sensitive than other methods. The results of this study may direct users towards a method appropriate to their T1‐weighted datasets and improve the efficiency of processing for large, multisite neuroimaging studies. Hum. Brain Mapping, 2005. © 2005 Wiley‐Liss, Inc.
Green leafy porridges: how good are they in controlling glycaemic response?

Green leafy porridges made with leaf water extracts, rice and coconut milk are common Sri Lankan dietary remedies for diabetes. Though water and ethanolic extracts of most leaves elicit hypoglycaemic effects, data are not available on the efficacy when leaf extracts are incorporated into porridges. Thus, an effort was made to evaluate the proximate compositions and glycaemic index (GI) of some commonly consumed green leafy porridges. The GI of rice porridge and coconut milk porridge were measured to evaluate the effect of other ingredients other than the leaf extracts. Rice was the main contributor to carbohydrate (56–68% on dry weight) and water was the main component in porridges (89–93%). Fat and total dietary fibre contents ranged between 2.5–27% and 5–10%, respectively. The GI of all porridges was low (GI ≤ 55), except Cassia auriculata which had a high GI of 77 ± 12. The GIs of coconut milk, Aerva lanata, Hemidesmus indicus, Scoparia dulcis, Asparagus racemosus, Cephalandra indica, Cardiospermum halicacabum, Murraya koenigii and Aegle marmelos were 31 ± 5, 32 ± 5, 40 ± 8, 39 ± 8, 37 ± 4, 49 ± 8, 46 ± 8, 44 ± 8 and 50 ± 8, respectively. All porridges had a low or medium glycaemic loads ( ≤ 19). However, peak blood glucose reductions of ≥ 25% were observed in all leafy and coconut milk porridges, except in C. auriculata and Atlantia zeylanica, when compared with the glucose control. Therefore, green leafy porridges, except Cassia, can be recommended as breakfast meals for diabetics due to their low GI, peak blood glucose reduction and presence of other nutrients in green leaves.
ArrayXPath: mapping and visualizing microarray gene-expression data with integrated biological pathway resources using Scalable Vector Graphics

Biological pathways can provide key information on the organization of biological systems. ArrayXPath (http://www.snubi.org/software/ArrayXPath/) is a web-based service for mapping and visualizing microarray gene-expression data for integrated biological pathway resources using Scalable Vector Graphics (SVG). By integrating major bio-databases and searching pathway resources, ArrayXPath automatically maps different types of identifiers from microarray probes and pathway elements. When one inputs gene-expression clusters, ArrayXPath produces a list of the best matching pathways for each cluster. We applied Fisher's exact test and the false discovery rate (FDR) to evaluate the statistical significance of the association between a cluster and a pathway while correcting the multiple-comparison problem. ArrayXPath produces Javascript-enabled SVGs for web-enabled interactive visualization of pathways integrated with gene-expression profiles.
Molecular genetics and diversity of primary biogenic aerosol particles in urban, rural, and high-alpine air

Molecular genetics and diversity of primary biogenic aerosol particles in urban, rural, and high-alpine air V. Després, J. Nowoisky, M. Klose, R. Conrad, M. O. Andreae, and U. Pöschl Biogeochemistry Department, Max Planck Institute for Chemistry, P.O. Box 3060, 55020 Mainz, Germany Biogeochemistry Department, Max Planck Institute for Terrestrial Microbiology, Karl-von-Frisch-Straße, 35043 Marburg, Germany Department of General Botany, Johannes Gutenberg University, Saarstraße 1, 55099 Mainz, Germany Received: 24 January 2007 – Accepted: 29 January 2007 – Published: 5 February 2007 Correspondence to: V. Després (despres@mpch-mainz.mpg.de)
Combining Acoustic and Multilevel Visual Features for Music Genre Classification

Most music genre classification approaches extract acoustic features from frames to capture timbre information, leading to the common framework of bag-of-frames analysis. However, time-frequency analysis is also vital for modeling music genres. This article proposes multilevel visual features for extracting spectrogram textures and their temporal variations. A confidence-based late fusion is proposed for combining the acoustic and visual features. The experimental results indicated that the proposed method achieved an accuracy improvement of approximately 14% and 2% in the world's largest benchmark dataset (MASD) and Unique dataset, respectively. In particular, the proposed approach won the Music Information Retrieval Evaluation eXchange (MIREX) music genre classification contests from 2011 to 2013, demonstrating the feasibility and necessity of combining acoustic and visual features for classifying music genres.
Assessing the Predictability of Hospital Readmission Using Machine Learning

Unplanned hospital readmissions raise health care costs and cause significant distress to patients. Hence, predicting which patients are at risk to be readmitted is of great interest. In this paper, we mine large amounts of administrative information from claim data, including patients demographics, dispensed drugs, medical or surgical procedures performed, and medical diagnosis, in order to predict readmission using supervised learning methods. Our objective is to gain knowledge about the predictive power of the available information. Our preliminary results on data from the provincial hospital system in Quebec illustrate the potential for this approach to reveal important information on factors that trigger hospital readmission. Our findings suggest that a substantial portion of readmissions is inherently hard to predict. Consequently, the use of the raw readmission rate as an indicator of the quality of provided care might not be appropriate.
Elevated Stress-Hemoconcentration in Major Depression Is Normalized by Antidepressant Treatment: Secondary Analysis from a Randomized, Double-Blind Clinical Trial and Relevance to Cardiovascular Disease Risk

Background Major depressive disorder (MDD) is an independent risk factor for cardiovascular disease (CVD); the presence of MDD symptoms in patients with CVD is associated with a higher incidence of cardiac complications following acute myocardial infarction (MI). Stress-hemoconcentration, a result of psychological stress that might be a risk factor for the pathogenesis of CVD, has been studied in stress-challenge paradigms but has not been systematically studied in MDD. Methods Secondary analysis of stress hemoconcentration was performed on data from controls and subjects with mild to moderate MDD participating in an ongoing pharmacogenetic study of antidepressant treatment response to desipramine or fluoxetine. Hematologic and hemorheologic measures of stress-hemoconcentration included blood cell counts, hematocrit, hemoglobin, total serum protein, and albumin, and whole blood viscosity. Findings Subjects with mild to moderate MDD had significantly increased hemorheologic measures of stress-hemoconcentration and blood viscosity when compared to controls; these measures were correlated with depression severity. Measures of stress-hemoconcentration improved significantly after 8 weeks of antidepressant treatment. Improvements in white blood cell count, red blood cell measures and plasma volume were correlated with decreased severity of depression. Conclusions Our secondary data analyses support that stress-hemoconcentration, possibly caused by decrements in plasma volume during psychological stress, is present in Mexican-American subjects with mild to moderate MDD at non-challenged baseline conditions. We also found that after antidepressant treatment hemorheologic measures of stress-hemoconcentration are improved and are correlated with improvement of depressive symptoms. These findings suggest that antidepressant treatment may have a positive impact in CVD by ameliorating increased blood viscosity. Physicians should be aware of the potential impact of measures of hemoconcentration and consider the implications for cardiovascular risk in depressed patients.
TELOS: An Approach for Distributed Graph Processing Based on Overlay Composition

The recent years have been characterised by the production of a huge amount of data. As matter of fact, human being, overall, is generating an unprecedented amount of data flowing in multiple and heterogeneous sources, ranging from scientific devices, social network, business transactions, etc. Data that is usually represented as a graph, which, due to its size, is often infeasible to process on a single machine. The direct consequence is the need for exploiting parallel and distributed computing frameworks to face this problem. This paper proposes TELOS, an high-level approach for large graphs processing. Our approach takes inspiration from overlay networks, that is a widely adopted approach for information dissemination, aggregation and computing orchestration in highly distributed systems. TELOS consists of a programming framework supporting the definition of computations on graphs as a composition of overlays, each devoted to a specific aim. The framework is implemented on the top of Apache Spark. A set of experimental results is presented to give a clear evidence of the effectiveness of our approach.
Methodology for Requirements Analysis and Design in Development of Service-Oriented Information Systems

Service orientation and Model Driven Development are the pillars of modern Information Systems. Service-oriented information systems are composed of loosely coupled and interoperable services driven by requirements of users. New modelling facilities are proposed to enable effective service development including more rigorous requirements definition, reconciliation and transformations of requirements to software architectures. These facilities are focused on modelling interactions and state transitions of services represented by interfaces and entities of problem domain. Metamodels for requirements and design model specification, algorithms for requirement reconciliation and transformation to design were created using UML and OCL, and implemented in UML CASE tools.
Joint suppression of PAPR and sidelobe of hybrid carrier communication system based on WFRFT

A novel joint suppression method of peak to average power ratio (PAPR) and sidelobe power of hybrid carrier (HC) system based on weighted-type fractional Fourier transform (WFRFT) is proposed in this paper. PAPR and sidelobe power are restrained through pulse shaping in frequency domain and windowing in time domain respectively. Undesired peak regeneration emerges as a result of time windowing for out of band radiation suppression and pulse shaping is applied as a united technique to suppress PAPR with scarcely affecting on system performance. Flexible selections of WFRFT parameter α, shaping parameter β, and windowing parameter γ are employed for multi-objective optimization of the proposed HC framework. A multichannel model is then proposed according to the parameter analysis.
Balance Between Genetic Search And Local Search In Hybrid Evolutionary Multi-criterion Optimization Algorithms

The aim of this paper is to clearly demonstrate the importance of finding a good balance between genetic search and local search in the implementation of hybrid evolutionary multi-criterion optimization (EMO) algorithms. We first modify the local search part of an existing multi-objective genetic local search (MOGLS) algorithm. In the modified MOGLS algorithm, the computation time spent by local search can be decreased by two tricks: to apply local search to only selected solutions (not all solutions) and to terminate local search before all neighbors of the current solution are examined. Next we show that the local search part of the modified MOGLS algorithm can be combined with other EMO algorithms. We implement a hybrid version of a strength Pareto evolutionary algorithm (SPEA). Using the modified MOGLS algorithm and the hybrid SPEA algorithm, we examine the balance between genetic search and local search through computer simulations on a two-objective flowshop scheduling problem. Computer simulations are performed using various specifications of parameter values that control the computation time spent by local search.
Efficient Texture-less Object Detection for Augmented Reality Guidance

Real-time scalable detection of texture-less objects in 2D images is a highly relevant task for augmented reality applications such as assembly guidance. The paper presents a purely edge-based method based on the approach of Damen et al. (2012) [5]. The proposed method exploits the recent structured edge detector by Dollár and Zitnick (2013) [8], which uses supervised examples for improved object outline detection. It was experimentally shown to yield consistently better results than the standard Canny edge detector. The work has identified two other areas of improvement over the original method; proposing a Hough-based tracing, bringing a speed-up of more than 5 times, and a search for edgelets in stripes instead of wedges, achieving improved performance especially at lower rates of false positives per image. Experimental evaluation proves the proposed method to be faster and more robust. The method is also demonstrated to be suitable to support an augmented reality application for assembly guidance.
Probabilistic models of similarity in syntactic context

This paper investigates novel methods for incorporating syntactic information in probabilistic latent variable models of lexical choice and contextual similarity. The resulting models capture the effects of context on the interpretation of a word and in particular its effect on the appropriateness of replacing that word with a potentially related one. Evaluating our techniques on two datasets, we report performance above the prior state of the art for estimating sentence similarity and ranking lexical substitutes.
3 . 1 Asymmetric Resource Thread Matching : Coarse Granularity Variability Management

Manufacturing related variations are predicted to have a significant impact on power consumption for near future technology generations. Due to both die-to-die and within-die parameter variations, the power consumption of identically designed hardware resources can vary randomly on a per instance basis. This introduces fabrication induced asymmetry, which can undermine basic assumptions about power/performance balances in a design. Specifically, architects expect that superscalar pipeline resources and cores in a homogenous CMP design are truly homogenous when fabricated on a chip. In this paper, we describe implications that fabrication power asymmetry have on a CMP design with respect to the allocation and utilization of hardware resources and the mapping between applications and physical cores. We introduce hardware/software management techniques that can overcome power asymmetry by considering the dissimilar power profiles of physical resources under parameter variation.
An antiarch placoderm shows that pelvic girdles arose at the root of jawed vertebrates

Almost all gnathostomes or jawed vertebrates (including osteichthyans, chondrichthyans, ‘acanthodians’ and most placoderms) possess paired pectoral and pelvic fins. To date, it has generally been believed that antiarch placoderms (extinct armoured jawed fishes from the Silurian–Devonian periods) lacked pelvic fins. The putative absence of pelvic fins is a key character bearing on the monophyly or paraphyly of placoderms. It also has far-reaching implications for studying the sequence of origin of pelvic girdles versus that of movable jaws in the course of vertebrate evolution. Parayunnanolepis xitunensis represents the only example of a primitive antiarch with extensive post-thoracic preservation, and its original description has been cited as confirming the primitive lack of pelvic fins in early antiarchs. Here, we present a revised description of Parayunnanolepis and offer the first unambiguous evidence for the presence of pelvic girdles in antiarchs. As antiarchs are placed at the base of the gnathostome radiation in several recent studies, our finding shows that all jawed vertebrates (including antiarch placoderms) primitively possess both pectoral and pelvic fins and that the pelvic fins did not arise within gnathostomes at a point subsequent to the origin of jaws.