Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling service orchestrations with a rule-enhanced business process language

Business process modeling has been a promising direction in developing service compositions, including both service orchestrations and choreographies. This paper fully focuses on the problem of modeling service orchestrations. Despite many promising aspects of using business process modeling (BPM) languages for modeling service orchestrations, this paper aims to demonstrate that: i) best practices (workflow patters) for control flows (primary concern of service orchestrations) are not fully covered in present languages; ii) complete service compositions cannot be completely generated from business process models; and iii) BPM languages have limited support for representing logical expressions, business vocabularies, and business rules, which severely limits their flexibility and expressivity. To address these challenges, we have integrated business rule modeling constructs of the REWERSE Rule Markup Language (R2ML) with the Business Process Modeling Notation (BPMN), resulting in our rBPMN proposal.
Content and function of the self-definition in old and very old age.

Spontaneous self-definition was investigated in a heterogeneous sample of N = 516 participants of the Berlin Aging Study, aged between 70 and 103 years. The content of the self-definition revealed that old and very old persons view themselves as active and present-oriented. The self-definition also reflected an inward orientation, and central themes of life-review, health, and family. Participants generated more positive than negative evaluations in their self-definition, but the ratio of positive to negative evaluations was less favorable for the oldest old (> or = 85 years) than that of persons aged 70 to 84 years. Older individuals with more health-related constraints reported fewer and less rich self-defining domains (i.e., a less multifaceted self-definition). Positive emotional well-being was associated with naming more and richer self-defining domains. Multifacetness, however, did not buffer against the negative effect of low functional capacity on subjective well-being.
Evaluation of the Immunogenicity of Diphtheria Toxoid Conjugated to Salmonella Typhimurium-Derived OPS in a Mouse Model: A Potential Vaccine Candidate Against Salmonellosis

Background Salmonella enterica serovar Typhimurium (S. Typhimurium) causes gastroenteritis in humans and paratyphoid disease in some animals. Given the emergence of antibiotic resistance, vaccines are more effective than chemotherapy in disease control. Objectives The aim of this experimental study was to evaluate the immunogenicity of diphtheria toxoid (DT) conjugated with S. Typhimurium -derived OPS (O side chain isolation) in mice to determine its potential as a vaccine candidate against salmonellosis. Materials and Methods Lipopolysaccharide (LPS) was extracted from the bacterial strain. After isolation of the O side chain of LPS, detoxification, and conjugation of the detoxified OPS samples with DT, pyrogenicity, toxicity, and sterility tests were performed. To vaccination, four groups of female Balb/c mice were used in an immunization test. Antibody responses were measured by the ELISA method. Challenging processes were performed to analyze the efficacy of the OPS-DT compound. Results Two weeks after the first vaccination dose, there was no significant difference in the antibody titers of the OPS and OPS-DT groups. However, after the second and third doses, the antibody titers of the OPS-DT group increased significantly compared with those of the control groups (P < 0.001). The induction of anti-OPS antibodies was as follows: OPS-DT>OPS. The most anti-OPS IgG antibody was IgG1. Challenging procedure showed successful protective characteristics in clinical examinations. Conclusions The results indicated that DT increased anti-OPS antibodies against the OPS-DT compound. The antibody response to OPS-DT was greater than that to OPS alone. We conclude that OPS-DT is an appropriate and acceptable vaccine candidate against salmonellosis.
Automatic three-dimensional segmentation of MR images applied to the rat uterus

We introduce an automatic 3D multiscale automatic segmentation algorithm for delineating specific organs in Magnetic Resonance images (MRI). The algorithm can process several modalities simultaneously, and handle both isotropic and anisotropic data in only linear time complexity. It produces a hierarchical decomposition of MRI scans. During this segmentation process a rich set of features describing the segments in terms of intensity, shape and location are calculated, reflecting the formation of the hierarchical decomposition. We show that this method can delineate the entire uterus of the rat abdomen in 3D MR images utilizing a combination of scanning protocols that jointly achieve high contrast between the uterus and other abdominal organs and between inner structures of the rat uterus. Both single and multi-channel automatic segmentation demonstrate high correlation to a manual segmentation. While the focus here is on the rat uterus, the general approach can be applied to recognition in 2D, 3D and multi-channel medical images.
Valuation of Recreation in the National Parks: Estimating Micro Meta Models for Benefit Transfer

The 397 units of the U.S. National Park System (NPS) are an extremely important U.S. recreational asset, supporting upwards of 280 million recreational visits annually (Street 2012) and driving the tourism economies of many U.S. regions (Stynes et al. 2000). Recreation values are an important policy analysis tool to inform management decisions affecting those park units. However, until very recently recreation values had only been directly estimated for a relatively small number of parks (Kaval and Loomis 2003; Duffield et al. 2009). Benefit transfer is an efficient approach for estimating values for a given park unit based on known values from sites that have been previously studied. This paper presents empirical estimates of benefit transfer functions for the U.S. National Park System based on stated preference survey responses from an NPS visitor survey administered at a cross-section of NPS park units. The study focuses on a subset of ecosystem service values including recreational, aesthetic, historical, and cultural values associated with on-site visitation. Left unaddressed is consideration of any passive use values.
Bounds on Non - Symmetric Divergence Measures in terms of Symmetric Divergence Measures - communica

A manual manipulation toy includes a circular platform having a plurality of upstanding spaced apart pins thereon adapted to engage and interrupt the progress of a rolling ball which is released at an upper level on the surface of the platform to roll towards a lower level while the platform is manually rotated in either direction to move the pins into engagement with the rolling ball thereby to retain the ball on the surface as long as possible. The platform is supported for rotation about an inclined, upstanding central axis and means is provided for selectively adjusting the angle of inclination and for counting the number of revolutions of the platform between the time when the ball is first released and subsequently rolls off an edge of the surface into a peripheral trough.
ON THE USE OF DYNAMIC INVERSION FOR THE IMPROVEMENT OF PID CONTROL

Abstract In this paper we discuss the use of a noncausal approach for the improvement of PID control, according to a method presented in (Piazzi and Visioli, 2004). In particular we verify that, despite the proposed methodology is based on an estimated first-order plus dead time (FOPDT) model of the process, different identification procedure can be employed for this purpose yielding in any case to a good result. Further, the role of the unique design parameter is analysed.
Return-limited inductances: a practical approach to on-chip inductance extraction

This paper proposes a practical approach for extracting approximate inductances of on-chip interconnect. This approach, which we call the method of return-limited inductances, is based on performing the inductance modeling of signal lines and power-ground lines independently and on taking advantage of the power-ground distribution of the chip to localize inductive coupling. Results from representative on-chip interconnect examples demonstrate the efficiency and accuracy of this approach over other sparsification techniques.
A dynamic look-ahead Monte Carlo algorithm for pricing Bermudan options

Under the assumption of no-arbitrage, the pricing of American and Bermudan options can be casted into optimal stopping problems. We propose a new adaptive simulation based algorithm for the numerical solution of optimal stopping problems in discrete time. Our approach is to recursively compute the so-called continuation values. They are defined as regression functions of the cash flow, which would occur over a series of subsequent time periods, if the approximated optimal exercise strategy is applied. We use nonparametric least squares regression estimates to approximate the continuation values from a set of sample paths which we simulate from the underlying stochastic process. The parameters of the regression estimates and the regression problems are chosen in a data-dependent manner. We present results concerning the consistency and rate of convergence of the new algorithm. Finally, we illustrate its performance by pricing high-dimensional Bermudan basket options with strangle-spread payoff based on the average of the underlying assets.
A Comparison of Empirical Models for Predicting Student Retention

Literature indicates that data mining or algorithmic approaches to prediction can provide superior results vis-à-vis traditional statistical modeling approaches (Delen et al, 2004; Sharda and Delen, 2006; Delen et al, 2007; Kiang 2007; Li et al 2009). However, little research in higher education has focused on the employment of data mining methods for predicting retention. Using three years of data for first time first year degree seeking students we compared results from logistic regression, decision trees, neural networks, and ensemble models implemented using SAS Enterprise Miner. Based on validation misclassification rates and robustness, decision trees were chosen for the final model specification. Building models for three time periods (pre-enrollment or 1 day of term, 5 week of term, and end of term) the predictive accuracy of our decision trees improved with each shift in time. The score code generated from SAS Enterprise Miner will make it possible to create custom reports with color coded risk indicators within the SAS BI decision support system. This will enable administrators, advisors, professors and other higher education professionals within the institution to incorporate advanced analytics into retention efforts.
Clinical Significance of Persistent Tumor in Bone Marrow during Treatment of High-risk Neuroblastoma

The records of 63 high-risk neuroblastoma patients with bone marrow (BM) tumors at diagnosis were retrospectively reviewed. All patients received nine cycles of induction chemotherapy followed by tandem high-dose chemotherapy and autologous stem cell transplantation (HDCT/auto-SCT). Follow-up BM examination was performed every three cycles during induction chemotherapy and every three months for one year after the second HDCT/auto-SCT. BM tumor cells persisted in 48.4%, 37.7%, 23.3%, and 20.4% of patients after three, six, and nine cycles of induction chemotherapy and three months after the second HDCT/auto-SCT, respectively. There was no difference in progression-free survival (PFS) rate between patients with persistent BM tumor and those without during the induction treatment. However, after tandem HDCT/auto-SCT, the PFS rate was worse in patients with persistent BM tumor than in those without (probability of 5-yr PFS 14.7% ± 13.4% vs. 64.2% ± 8.3%, P = 0.009). Persistent BM tumor during induction treatment is not associated with a worse prognosis when intensive tandem HDCT/auto-SCT is given as consolidation treatment. However, persistent BM tumor after tandem HDCT/auto-SCT is associated with a worse prognosis. Therefore, further treatment might be needed in patients with persistent BM tumor after tandem HDCT/auto-SCT. Graphical Abstract
Extending BART to Provide a Coreference Resolution System for German

We present a flexible toolkit-based approach to automatic coreference resolution on German text. We start with our previous work aimed at reimplementing the system from Soon et al. (2001) for English, and extend it to duplicate a version of the state-of-the-art proposal from Klenner and Ailloud (2009). Evaluation performed on a benchmarking dataset, namely the TueBa-D/Z corpus (Hinrichs et al., 2005b), shows that machine learning based coreference resolution can be robustly performed in a language other than English.
Robust Feature Extraction Using Modulation Filtering of Autoregressive Models

Speaker and language recognition in noisy and degraded channel conditions continue to be a challenging problem mainly due to the mismatch between clean training and noisy test conditions. In the presence of noise, the most reliable portions of the signal are the high energy regions which can be used for robust feature extraction. In this paper, we propose a front end processing scheme based on autoregressive (AR) models that represent the high energy regions with good accuracy followed by a modulation filtering process. The AR model of the spectrogram is derived using two separable time and frequency AR transforms. The first AR model (temporal AR model) of the sub-band Hilbert envelopes is derived using frequency domain linear prediction (FDLP). This is followed by a spectral AR model applied on the FDLP envelopes. The output 2-D AR model represents a low-pass modulation filtered spectrogram of the speech signal. The band-pass modulation filtered spectrograms can further be derived by dividing two AR models with different model orders (cut-off frequencies). The modulation filtered spectrograms are converted to cepstral coefficients and are used for a speaker recognition task in noisy and reverberant conditions. Various speaker recognition experiments are performed with clean and noisy versions of the NIST-2010 speaker recognition evaluation (SRE) database using the state-of-the-art speaker recognition system. In these experiments, the proposed front-end analysis provides substantial improvements (relative improvements of up to 25%) compared to baseline techniques. Furthermore, we also illustrate the generalizability of the proposed methods using language identification (LID) experiments on highly degraded high-frequency (HF) radio channels and speech recognition experiments on noisy data.
Antimicrobial Activity of Urine after Ingestion of Differing Daily Doses of Cranberry Juice Cocktail in Pregnancy: A Pilot Study~!2010-03-17~!2010-04-29~!2010-06-03~!

The objective of this study is to evaluate in vitro antimicrobial activity of urine from pregnant subjects following cranberry juice cocktail (CJC) or placebo ingestion against common pathogens causing asymptomatic bacteriuria. This study was conducted as an adjunct to a larger randomized, controlled trial. 4-hour uninfected urine samples were collected from 28 pregnant women who were randomized to cranberry or placebo in three groups: A. CJC 240 ml two times daily (C, C; n=10), B: CJC in the AM, then placebo in the PM (C, P; n=10), C: placebo two times daily (P, P; n=8). The pH of all specimens was adjusted to 7 and filtered. Aliquots were independently inoculated with overnight culture of 10 2-3 cell/ml each of single strains of E. coli with both type I and type II fimbriae, K. pneumoniae, and C. albicans, and incubated. CFU/ml of each specimen was enumerated by subculture with quantitative plate counts in duplicate. There were no differences between groups for any of the pathogens studies based on treatment allocation. We demonstrated no differences in direct antimicrobial activity against E. coli, K. pneumonia or C. albicans in the urine of pregnant based on differing daily cranberry doses. This may be due to beta-error.
Second-Order Extended  $H_{\infty}$ Filter for Nonlinear Discrete-Time Systems Using Quadratic Error Matrix Approximation

In this paper, the second-order extended (SOE) H∞ filter for nonlinear discrete-time systems is derived based on an approximation to the quadratic error matrix. The solution is obtained by the game theory approach. It is shown that the result bears a strong resemblance to the SOE Kalman filter when the performance bound goes to infinity. An example of vehicle state tracking is simulated to compare the performances of the SOE Kalman filter, the first order extended and the SOE H∞ filter. Noises with unknown bias are injected into both process dynamics and measurements. The results show that the SOE H∞ filter has the smallest state tracking error.
A Capture Scenario for the Globular Cluster ω Centauri

We explore an accretion origin for ω Cen by N-body modeling of the orbital decay and disruption of a Milky Way dwarf satellite. This work is focused on studying a particular satellite model that aims to reproduce the present orbit of ω Cen, as recently determined from absolute proper motions. The model satellite is launched from 58 kpc from the Galactic center, on a radial low-inclination orbit. We find that a capture scenario can produce an ω Cen-like object with the current low-energy orbit of the cluster. Our best model is a nucleated dwarf galaxy with a Hernquist density profile that has a mass of 8 × 109 M☉ and a half-mass radius of 1.4 kpc.
2016 Ieee International Conference on Big Data (big Data) an Adaptive Information-theoretic Approach for Identifying Temporal Correlations in Big Data Sets

—In the past two decades, new developments in computing, sensing and crowdsourced data have resulted in an explosion in the availability of quantitative information. The possibilities of analyzing this so-called " big data " to inform research and the decision-making process are virtually endless. In general analyses have to be done across multiple data sets in order to bring out the most value of big data. A first important step is to identify temporal correlations between data sets. Given the characteristics of big data in term of volume and velocity, techniques that identify correlations not only need to be scalable, but also need to help users in ordering the correlation across temporal resolutions so that they can focus on important relationships. There is a large body of work in this area, however, most of them either only deal with small data sets, using a fixed temporal resolution, or does not provide a quantifiable measure of a correlation significance. In this paper, we present a method based on mutual information to identify correlations in large data sets. Discovered correlations are suggested to users in an order based on their significance. Our method supports an adaptive streaming technique that minimizes duplicated computation and is implemented on top of Apache Spark for scalability using big data platforms. We also provide a comprehensive evaluation using real-world data sets from NYC Open Data, and compare our findings against a recent study. I. INTRODUCTION The true value of big data comes from analyzing multiple data sets, in which discovering correlations among them is one of the first steps towards creating new values in big data applications. As data originally reside in individual silos, each of them may serve for a specific and/or limited purpose. However, their combination can, and often does, offer new insights into important problems. In particular, data correlation can result in the identification of individual events and phenomena, as well as the creation of profiles to track these activities even in real-time. Data correlation is also useful in constructing and validating behavioral proxies. For example, demonstrating that the traffic speed is well-correlated with the number of taxi pickups through historical data sources for which the latter are known directly
Drosophila spectrin. II. Conserved features of the alpha-subunit are revealed by analysis of cDNA clones and fusion proteins

Drosophila alpha-spectrin cDNA sequences were isolated from a lambda gt11 expression library. These cDNA clones encode fusion proteins that include portions of the Drosophila alpha-spectrin polypeptide as shown by a number of structural and functional criteria. The fusion proteins elicited antibodies that reacted strongly with Drosophila and vertebrate alpha-spectrins and a comparison of cyanogen bromide peptide maps demonstrated a clear structural correspondence between one fusion protein and purified Drosophila alpha-spectrin. Alpha-spectrin fusion protein also displayed calcium-dependent calmodulin-binding activity in blot overlay experiments and one fusion protein bound specifically to both Drosophila and bovine brain beta-spectrin subunits on protein blots. A region of the Drosophila cDNA cross-hybridized at lowered stringency with an avian alpha-spectrin cDNA. Together these data show that the composition, structure, and binding properties of the spectrin family of proteins have been remarkably well conserved between arthropods and vertebrates. Drosophila cDNA hybridized to an mRNA of greater than or equal to 9 kb on blots of total Drosophila poly A+ RNA; and hybridized in situ to a single site in polytene region 62B, 1-7. This result and Southern blot analysis of genomic DNA indicate that the sequences are likely to be single copy in the Drosophila genome.
Data Driven Evolutionary Optimization of Complex Systems: Big Data Versus Small Data

This invited talk Existing evolutionary algorithms typically assume that there are explicit objective functions available for (cid:12)tness evaluations. In the real world, such explicit objec-tive functions may not be available in many cases. For example, many industrial optimization problems such as structural design [8] need to perform computationally very intensive numerical simulations, such as computational (cid:13)uid dynamic simulations or (cid:12)nite element analysis, where a large set of partial diﬀerential equations must be solved. In many process industry optimization problems, no explicit models exist for describing the relationship between the (cid:12)nal quality of the product and the decision variables, such as temperature and humidity. Thus, only historical experimental data can be used for optimization. There are also cases where only factual data can be collected. A good example of such optimization problems is trauma systems design [11], where only patient records are available for optimization. For solving such optimization problems, evolutionary optimization can be conducted only using a data-driven approach. The main challenges in data-driven evolutionary optimization can roughly be divided into two categories according to the amount of available data, namely, small data and big data. The lack of data can mainly be attributed to the fact that data acquisition is very expensive, either computationally or costly. In [11], data-driven evolutionary optimization problems are divided into two paradigms, one termed oﬀ-line data-driven optimization, where no new data can be actively sampled, and the other on-line data-driven optimization, where a small number of new data points can be collected. For small data driven evolutionary optimization, the use of surrogate techniques to assist evolutionary algorithms becomes indispensable [6, 7]. In particular in on-line data-driven surrogate-assisted evolutionary optimization, two important questions arise. The (cid:12)rst question is, which surrogate