Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Natural Classification Kernels Using Prior Knowledge

A new class of kernels has been designed to enhance the usability of prior knowledge. Prior knowledge is shown to improve the generalization ability of kernel algorithms for binary classification problems. The prior knowledge is expressed in natural language via fuzzy rules. First, the concepts of a fuzzy rule base and prior-confidence region are proposed to formulate the prior knowledge. Then, the new kernels, which are referred to as natural classification kernels (NCKs), are represented by fuzzy equivalence relations based on the formulation of prior knowledge. An NCK is interpreted as a measure of similarities between samples. It is proven that NCKs have two desired properties: 1) transitivity with respect to the triangular norms and 2) the ability to provide higher similarities to spatially closer samples from the same class. Using transitivity, a large number of NCKs may be directly obtained by means of triangular norms. Additionally, the theoretical results show that the second property makes it possible for the support vector machine (SVM) and convex hull separation algorithm to generalize from training samples to test samples in the prior-confidence region. Finally, some synthetic datasets and a benchmark dataset are employed to validate the proposed approach.
Novel Noninvasive Breath Test Method for Screening Individuals at Risk for Diabetes

OBJECTIVE—Diagnosis of pre-diabetes and early-stage diabetes occurs primarily by means of an oral glucose tolerance test (OGTT), which requires invasive blood sampling. The aim of this study was to determine whether differences exist in breath 13CO2 excretion during a 13C-labeled OGTT between individuals with normal glucose tolerance (NGT) and individuals with pre-diabetes and early-stage diabetes (PDED) and whether these differences correlated with blood glucose kinetics. RESEARCH DESIGN AND METHODS—Blood and breath samples were collected at baseline and every 30 min for a 10-h period after ingestion of 75 g glucose isotopically labeled with 150 mg [U-13C6]d-glucose. RESULTS—Age (56 ± 5 vs. 47 ± 3 years) and BMI (31 ± 2 vs. 31 ± 2 kg/m2) were not different between individuals with NGT (n = 10) and PDED (n = 7), respectively. Blood glucose concentrations were significantly higher in those with PDED compared with those with NGT from baseline to 4.5 h after glucose ingestion (P ≤ 0.05). Glucose-derived breath 13CO2 was significantly lower in individuals with PDED compared with those with NGT from 1 to 3.5 h after glucose (P ≤ 0.05). Peak breath 13CO2 abundance occurred at 4.5 and 3.5 h in individuals with PDED and NGT, respectively (36.87 ± 3.15 vs. 41.36 ± 1.56‰ delta over baseline). CONCLUSIONS—These results suggest that this novel breath test method may assist in recognition of pre-diabetes or early-stage diabetes in at-risk persons without the need for invasive blood sampling, thus making it an attractive option for large-scale testing of at-risk populations, such as children.
Histamine activates tyrosine hydroxylase in bovine adrenal chromaffin cells through a pathway that involves ERK1/2 but not p38 or JNK

In bovine adrenal chromaffin cells (BACC) histamine promotes a rapid increase in the intracellular levels of Ca2+ together with the release of catecholamines and the phosphorylation of the catecholamine biosynthetic enzyme tyrosine hydroxylase (TH). In this study we investigated the role of the mitogen‐activated protein kinases (MAPK) extracellular signal‐regulated kinases (ERK1/2), stress activated protein kinase (p38) and Jun N‐terminal kinases (JNK) on the histamine‐induced activation and phosphorylation of TH. We found that in BACC histamine produced a rapid, long lasting and histamine type‐1 (H1) receptor‐dependent increase in the phosphorylation levels of ERK1/2, p38 and JNK which was accompanied by a H1 receptor‐dependent increase in TH activity. This increase in TH activity was partially blocked by the MEK1/2 inhibitor U0126 but was unaffected by the p38 antagonist SB203580 or the JNK blocker JNKI1. To study the effect of MAPK inhibition on histamine‐induced TH phosphorylation, we generated phospho‐specific antibodies against the different phosphorylated forms of TH. Treatment with U0126 totally inhibited the histamine‐induced phosphorylation of TH at Ser31, without affecting the phosphorylation of either Ser40 or Ser19. Neither SB203580 nor JNKI1 treatments produced any significant modification of the histamine‐induced TH phosphorylation. Our data support the hypothesis that the up‐regulation of the ERK1/2 pathway, but not that of p38 or JNK, promoted by histamine is involved in the phosphorylation of TH at Ser31 and that this phosphorylation event is required for the full activation of this enzyme.
Differential Secondary Reconstitution of In Vivo-Selected Human SCID-Repopulating Cells in NOD/SCID versus NOD/SCID/γ chainnull Mice

Humanized bone-marrow xenograft models that can monitor the long-term impact of gene-therapy strategies will help facilitate evaluation of clinical utility. The ability of the murine bone-marrow microenvironment in NOD/SCID versus NOD/SCID/γ chainnull mice to support long-term engraftment of MGMTP140K-transduced human-hematopoietic cells following alkylator-mediated in vivo selection was investigated. Mice were transplanted with MGMTP140K-transduced CD34+ cells and transduced cells selected in vivo. At 4 months after transplantation, levels of human-cell engraftment, and MGMTP140K-transduced cells in the bone marrow of NOD/SCID versus NSG mice varied slightly in vehicle- and drug-treated mice. In secondary transplants, although equal numbers of MGMTP140K-transduced human cells were transplanted, engraftment was significantly higher in NOD/SCID/γ chainnull mice compared to NOD/SCID mice at 2 months after transplantation. These data indicate that reconstitution of NOD/SCID/γ chainnull mice with human-hematopoietic cells represents a more promising model in which to test for genotoxicity and efficacy of strategies that focus on manipulation of long-term repopulating cells of human origin.
Experimental Validations of a Simple PCB Interconnect Model for High-Rate Signal Integrity

This paper is devoted to investigating experimental validations of a simple modeling method of PCB interconnects for high-speed signal integrity and electromagnetic compatibility applications. Based on the theoretical approach using the extraction method of the interconnect per-unit-length RLCG parameters, the reduced models of the corresponding transfer function, -parameters, and access and transfer impedances are also established. The methodology describing the different steps of the technique proposed for practical use cases in ultra wideband is established. To verify the effectiveness of the concept under consideration, time-domain validations from the frequency-measured data are realized by using printed circuit board microstrip interconnect lines with micrometers width and millimeters long. Therefore, the model was first validated experimentally, and with electromagnetic simulations, in frequency domain via comparison of -parameters and -matrix from dc to some gigahertz. It was found that relative errors lower than 1 dB were evaluated between the insertion loss of the models, simulations, and measurements. Then, by injecting noisy digital- and mixed-signals with 1 Gigasymbol/s, relative errors of lower than 1% were evaluated by considering the time-domain responses. Compared to the existing interconnect modeling tools, the developed one presents a high accuracy, simplicity, and very less computation time.
Recommendation Diversification Using Explanations

We introduce the novel notion of {\em explanation-based diversification} to address the well-known problem of {\em over-specialization} in item recommendations. Over-specialization in recommender systems leads to result sets with items that are too similar to one another, thus reducing the diversity of results and limiting user choices. Traditionally, the problem is addressed through {\em attribute-based diversification}|grouping items in the result set that share many common attributes (e.g., genre for movies) and selecting only a limited number of items from each group. It is, however, not always applicable, especially for social content recommendations. For example, attributes may not be available as in the case of recommending URLs for users of del.icio. us. Explanation-based diversification provides a novel and complementary alternative|it leverages the {\em reason for which a particular item is being recommended} (i.e., explanation)|for diversifying the results, without the need to access the attributes of the items. In this paper, we formally define the problem of {\em explanation-based diversification} and, without going into the details of the actual diversification process, demonstrate its effectiveness on a real world data set, Yahoo! ~Movies.
Investigating antecedents to the development of competence and fulfillment among intellectually gifted adolescents: The validity of conjointly applying above-level ability and preference assessment for early educational and career planning

Using the Theory of Work Adjustment (Dawis & Lofquist, 1984; Lofquist & Dawis, 1991) and C. P. Snow's (1959) conceptualization of "two cultures" as theoretical frameworks, this paper analyzed the incremental validity of above-level preference assessment (relative to abilities) in predicting humanities, math-science, or other college majors completed by the intellectually gifted. Discriminant analysis resiilts indicated that age-13 SAT and Study of Values assessments of 432 intellectually gifted adolescents each provided unique and valuable information to the prediction of type of college major completed 10 years after initial assessment. These positive findings add to growing support for the notion of early crystallization of preferences among the gifted and lend fiuther credence to applying above-level ability and preference assessment in facilitating educational and early career planning among intellectually gifted adolescents.
A Rapid Match Algorithm for Continuous Speech Recognition

This paper describes an algorithm for performing rapid match on continuous speech that makes it possible to recognize sentences from an 842 word vocabulary on a desktop 33 megahertz 80486 computer in near real time. This algorithm relies on a combination of smoothing and linear segmentation together with the notion of word start groups. It appears that the total computation required grows more slowly than linearly with the vocabulary size, so that larger vocabularies appear feasible, with only moderately enhanced hardware. The rapid match algorithm described here is closely related to the one that is used in DragonDictate, Dragon's commercial 30,000 word discrete utterance recognizer.
RPT: Re-architecting Loss Protection for Content-Aware Networks

We revisit the design of redundancy-based loss protection schemes in light of recent advances in content-aware networking. Content-aware networks minimizes the overhead of redundancy, if the redundancy is introduced in a way that the network can understand. With this insight, we propose a new loss protection scheme called redundant packet transmission (RPT). Using redundant video streaming as an example, we show that our approach, unlike FEC in traditional networks, provides low latency with high robustness and is insensitive to parameter selection. We tackle practical issues such as minimizing the impact on other traffic and the network. We show that RPT provides a simple and general mechanism for application-specific control and flow prioritization.
Assessing the Accuracy of an Algorithm for the Estimation of Spatial Gait Parameters Using Inertial Measurement Units: Application to Healthy Subject and Hemiparetic Stroke Survivor

We have reviewed and assessed the reliability of a dead reckoning and drift correction algorithm for the estimation of spatial gait parameters using Inertial Measurement Units (IMUs). In particular, we are interested in obtaining accurate stride lengths measurements in order to assess the effects of a wearable haptic cueing device designed to assist people with neurological health conditions during gait rehabilitation. To assess the accuracy of the stride lengths estimates, we compared the output of the algorithm with measurements obtained using a high-end marker-based motion capture system, here adopted as a gold standard. In addition, we introduce an alternative method for detecting initial impact events (i.e. the instants at which one foot contacts the ground, here used for delimiting strides) using accelerometer data. Our method, based on a kinematic feature we named 'jerkage', has proved more robust than detecting peaks on raw accelerometer data. We argue that the resulting measurements of stride lengths are accurate enough to provide trend data needed to support worthwhile gait rehabilitation applications. This approach has potential to assist physiotherapists and patients without access to fully-equipped movement labs. More specifically, it has applications for collecting data to guide and assess gait rehabilitation both outdoors and at home.
On the geometry of output-code multi-class learning

We provide a new perspective on the popular multi-class algorithmic techniques one-vs-all and (error correcting) output-codes. We show that is that in cases where they are successful (at learning from labeled data), these techniques implicitly assume structure on how the classes are related. We show that by making that structure explicit, we can design algorithms to recover the classes based on limited labeled data. We provide results for commonly studied cases where the codewords of the classes are well separated: learning a linear one-vs-all classifier for data on the unit ball and learning a linear error correcting output code when the Hamming distance between the codewords is large (at least $d+1$ in a $d$-dimensional problem). We additionally consider the more challenging case where the codewords are not well separated, but satisfy a boundary features condition.
Natural language processing Laboratory Plan: An essential for Persian language

Prevalent use of human language in computer systems and language oriented document processing, especially in the web, caused creating the need of designing mechanisms for developing natural language processing. In this paper, a survey in Natural Language Processing Laboratory has been done and a proposed framework for its development is suggested. Achieving this goal, the specifications, structures and activities of available Natural Language Processing Laboratories have been reviewed and valuable results are obtained. Afterward, a proposed framework is established. This framework introduces requirements of Laboratory in a structural way that providing solutions for it any clear plan in NLP Laboratories. Finally, according to National requirements of Iran and characteristics of Persian language, a Natural Language Processing Laboratory in discussed framework is designed. The activities of this laboratory includes basic tasks like creating linguistic resources (Corpora, Bilingual corpora, Lexicon, etc), test datasets, developing Natural Language Processing System's Evaluation Framework, development of basic tools (Parser, POS-tagger, etc), research tasks like defining areas for universities, and services such as application development, natural language processing system evaluation, and granting of the appropriate certificates.
Reliability of the Pictorial Scale of Perceived Movement Skill Competence in 2 Diverse Samples of Young Children.

BACKGROUND The purpose was to determine the reliability of an instrument designed to assess young children's perceived movement skill competence in 2 diverse samples. METHODS A pictorial instrument assessed 12 perceived Fundamental Movement Skills (FMS) based on the Test of Gross Motor Development 2nd edition. Intra-Class Correlations (ICC) and internal consistency analyses were conducted. Paired sample t tests assessed change in mean perceived skill scores. Bivariate correlations between the intertrial difference and the mean of the trials explored proportional bias. RESULTS Sample 1 (S1) were culturally diverse Australian children (n = 111; 52% boys) aged 5 to 8 years (mean = 6.4, SD = 1.0) with educated parents. Sample 2 (S2) were racially diverse and socioeconomically disadvantaged American children (n = 110; 57% boys) aged 5 to 10 years (mean = 6.8, SD = 1.1). For all children, the internal consistency for 12 FMS was acceptable (S1 = 0.72, 0.75, S2 = 0.66, 0.67). ICCs were higher in S1 (0.73) than S2 (0.50). Mean changes between trials were small. There was little evidence of proportional bias. CONCLUSION Lower values in S2 may be due to differences in study demographic and execution. While the instrument demonstrated reliability/internal consistency, further work is recommended in diverse samples.
Association analysis of the interleukin 17A gene in Caucasian rheumatoid arthritis patients from Norway and New Zealand.

OBJECTIVE Elevated levels of IL-17A have been detected in the inflamed synovium of RA patients, and murine arthritis models deficient in IL17A have shown reduced inflammation. Our aim was to investigate IL17A as a candidate gene for RA, and to assess correlations between risk variants and disease phenotypes. METHODS Five single nucleotide polymorphisms (SNPs) were selected to tag the genetic variability of the IL17A region and were genotyped by TaqMan technology on 950 RA cases and 933 random controls from Norway. Associations to progression of radiographic damage and presence of autoantibodies were examined in a 10-yr follow-up cohort of early RA. In addition, 580 RA patients and 504 controls from New Zealand were used as a replication data set. RESULTS A weak association between RA and the promoter SNP rs2275913 [odds ratio (OR) = 1.17; 95% CI 1.02, 1.34; P = 0.02] was found in the Norwegian population. The association was also evident at the genotype level where it indicated a recessive model. The allelic association was not replicated in the RA cohort from New Zealand (OR = 0.96; 95% CI 0.81, 1.16; P = 0.69). However, combined analysis suggested a weak recessive association (OR = 1.19; 95% CI 1.02, 1.37; P = 0.02). No significant associations were observed with radiographic progression, anti-cyclic citrullinated peptide or IgM-RF. CONCLUSIONS Modest evidence of an association with IL17A in Norwegian RA patients was observed. Although, our findings were not replicated in an independent RA material from New Zealand, a significant common risk estimate indicated that IL17A warrants further investigation in RA.
A scene recognition algorithm based on covariance descriptor

Covariance descriptor which is a good method for object detection represents a region of interest by using the covariance of d-features. Different scene images are regarded as textures with different labels in this paper, and then a novel scene recognition algorithm based on covariance descriptor is proposed. Firstly, the covariance feature of scene image is retrieved. Secondly, support vector machine is employed for training the combination classifiers which recognize the scene images. The experimental results demonstrate the feasibility and validity of the proposed algorithm.
Clinical and Genetic Correlates of Aldosterone-to-Renin Ratio and Relations to Blood Pressure in a Community Sample

Aldosterone:renin ratio (ARR) is used to screen for hyperaldosteronism. Data regarding correlates of ambulatory ARR in the community and its relation to hypertension incidence are limited. We defined clinical correlates of ARR, determined its heritability, tested for association and linkage, and related ARR to blood pressure (BP) progression in nonhypertensive individuals among 3326 individuals from the Framingham Heart Study (53% women; mean age: 59 years). Ambulatory morning ARR (serum aldosterone and plasma renin concentrations) were related to clinical covariates, genetic variation across the REN locus, a 10-cM linkage map, and among nonhypertensive participants (n=1773) to progression of ≥1 Sixth Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure BP category (optimal: <120/80 mm Hg, normal: 120 to 129/80 to 84 mm Hg, high normal: 130 to 139/85 to 89 mm Hg, hypertension: ≥140/90 mm Hg), or incident hypertension (systolic BP: ≥140 mm Hg, diastolic BP: ≥90 mm Hg, or use of antihypertensive treatment). ARR was positively associated with age, female sex, untreated hypertension, total/high-density lipoprotein cholesterol ratio, hormone replacement therapy, and &bgr;-blocker use, but negatively associated with angiotensin-converting enzyme inhibitor and diuretic use. ARR was heritable (h2=0.40), had modest linkage to chromosome 11p (logarithm of the odds: 1.89), but was not associated with 17 common variants in REN (n=1729). On follow-up (mean: 3 years), 607 nonhypertensive individuals (34.2%) developed BP progression, and 283 (16.0%) developed hypertension. Higher baseline logARR was associated with increased risk of BP progression (odds ratio per SD increment: 1.23; 95% CI: 1.11 to 1.37) and hypertension incidence (odds ratio per SD increment: 1.16; 95% CI: 1.00 to 1.33). ARR is a heritable trait influenced by clinical and genetic factors. There is a continuous gradient of increasing risk of BP progression across ARR levels in nonhypertensive individuals.
Prune the set of SV to improve the generalization performance of SVM

Initiated by that the quality of training data may affect the model selection, this paper presents a method to improve the prediction performance of SVM through pruning the set of SV. That is, using a global comparable noise measure based on neighbor distribution information to identify noisy SVs, and weaken their role in training. The difference of this method from traditional one is that it need not to process noise for every instance in training set, and but only for those in SVs. The experiment result shows that when top noisy SVs are weakened the prediction performance of SVM is better for most categories.
Relational Object Maps for Mobile Robots

Mobile robot map building is the task of generating a model of an environment from sensor data. Most existing approaches to mobile robot mapping either build topological representations or generate accurate, metric maps of an environment. In this paper we introduce relational object maps, a novel approach to building metric maps that represent individual objects such as doors or walls. We show how to extend relational Markov networks in order to reason about a hierarchy of objects and the spatial relationships between them. Markov chain Monte Carlo is used for efficient inference and to learn the parameters of the model. We show that the spatial constraints modeled by our mapping technique yield drastic improvements for labeling line segments extracted from laser range-finders.