Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic Feature-Based Korean Classifier Module for MT Systems

In this paper we propose a Korean numeral classifier system (KCL-SYS), a sub-module for generating the relation(s) between a Korean numeral classifier and its co-occurring noun(s), using semantic features and the LUB (Least Upper Bound) based on semantic hierarchies extracted from KorLex1Noun 1.5, to be applied to preprocessing in MT (MachineTranslation) systems. The ratio of recall in matching classifier-noun(s) was 79.51%, and the precision was 99.52%.
Low-frequency word enhancement with similar pairs in speech recognition

In practical automatic speech recognition (ASR) systems, it is difficult to recognize words that are with low-frequency in the language model (LM) training data. Ironically, these words tend to be highly important as they are often domain-specific name entities. In order to meet this challenge, we present a novel approach that enhances the weights of these words by borrowing information from some high-frequency words that are similar to the target words. Experimental results demonstrated that our method can significantly improve ASR performance on low-frequency words and does not impact performance on high-frequency words. Additionally, this method can be easily extended to deal with new words that are absent in the LM training data.
Economic Reforms and the Poor

This paper analyzes the effects of economic reforms for different income groups. My interest is spurred by the international debate on the social consequences of reforms and the potential adverse effects on poverty in particular. I find that the poor are in general positively affected by trade reforms, inflationary control, and structural reforms, while government consumption reduction affects the poorest income quintile negatively. In countries having undertaken World Bank financed reforms actual income of the poor was higher than predicted. Moreover, as the impact of reforms was strongest on the poor, World Bank support appears to reduce income inequality.
Ultimatum Proposals - How do Decisions Emerge?

The basic idea of decision emergence is to generate the prefered decision alternative instead of assuming it as exogeneously given like in neo-classical economics and game theory. The particular problem of ultimatum proposals has been selected in order to rely on experimental observations when speculating how choices emerge. Our approach distinguishes primary and secondary concerns of ultimatum proposers and illustrates how qualitative learning can (re)shape preferences over choices. ¤Preliminary ideas of this study were presented at a ZIF-Workshop, University of Bielefeld, in August 1994. I thank the participants, especially Reinhard Selten, for an inspiring discussion as well as Ste¤en Huck and Eric van Damme for helpful comments. Support from the Deutsche Forschungsgemeinschaft (SFB 373, Quanti kation und Simulation ökonomischer Prozesse) is gratefully acknowledged. yHumboldt-University of Berlin, Department of Economics, Institute for Economic Theory III, Spandauer Str. 1, D 10178 Berlin, Germany
In vivo antitumor activity of bis(4,7-dimethyl-1,10-phenanthroline) sulfatooxovanadium(IV) (METVAN [VO(SO4)(Me2-Phen)2]).

The compound bis(4,7-dimethyl-1,10-phenanthroline) sulfatooxovanadium(IV) (METVAN [VO(SO4)(Me2-Phen)2]), exhibits potent cytotoxicity against human cancer cells at low micromolar concentrations. At concentrations > or = 1 microM, METVAN treatment was associated with a nearly complete loss of the adhesive, migratory, and invasive properties of the treated tumor cell populations. METVAN did not cause acute or subacute toxicity in mice at dose levels ranging from 12.5 mg/kg to 100 mg/kg. Therapeutic plasma concentrations > or = 5 microM were rapidly achieved and maintained in mice for at least 24 h after i.p. bolus injection of a single 10 mg/kg nontoxic dose of METVAN. At this dose level, the maximum plasma METVAN concentration was 37.0 microM, which was achieved with a t(max) of 21.4 min. Plasma samples (diluted 1:16) from METVAN-treated mice killed 85% of human breast cancer cells in vitro. METVAN was slowly eliminated with an apparent plasma t(1/2) of 17.5 h and systemic clearance of 42.1 ml/h/kg. In accordance with its potent in vitro activity and favorable in vivo pharmacokinetics, METVAN exhibited significant antitumor activity and delayed tumor progression in CB.17 severe combined immunodeficient (SCID) mouse xenograft models of human glioblastoma and breast cancer. In these experiments, METVAN was administered in daily injections of a single nontoxic 10 mg/kg i.p. dose on 5 consecutive days per week for 4 consecutive weeks beginning the day after the s.c. inoculation of U87 glioblastoma or MDA-MB-231 breast cancer cells. At 40 days after the inoculation of tumor cells, the U87 tumor xenografts in the vehicle-treated control SCID mice were much larger than those of the mice treated with METVAN (4560 +/- 654 mm(3) versus 1688 +/- 571 mm(3); P = 0.003). Similarly, the MDA-MB-231 tumors in SCID mice treated with METVAN were much smaller 40 days after tumor cell inoculation than those of the vehicle-treated control SCID mice (174 +/- 29 mm(3) versus 487 +/- 82 mm(3); P = 0.002). The favorable in vivo pharmacodynamic features and antitumor activity of METVAN warrants further development of this novel oxovanadium compound as a potential new anticancer agent.
Intelligent Fingerprint Recognition System by using geometry approach

In this paper, we have presented a Fingerprint Recognition System which combines both the features extraction by applying a statistical and geometry approach. The core point (CP) of the input fingerprint is detected. Core point has been located in the center, the fingerprint image is cropped (A × A) around the based point. A fingerprint image is preprocessed for minutiae and core point. The core point is used to align the input image and template image. Fingerprint features such as minutiae points determination, their coordinates location, distance between base point and above minutiae points and radius of arcs for each ridge pattern based on the align fingerprint image. These above mentioned features are stored in different databases. The input fingerprint features are compared with above pre-defined database and decision will be made by voting system. We conducted evaluations on the FVC-2004 datasets and have summarized the results in the conclusion section.
Allogeneic Transplantation of Periodontal Ligament-Derived Multipotent Mesenchymal Stromal Cell Sheets in Canine Critical-Size Supra-Alveolar Periodontal Defect Model

Abstract Periodontitis is a chronic inflammatory disease that induces the destruction of tooth-supporting tissues, followed by tooth loss. Although several approaches have been applied to periodontal regeneration, complete periodontal regeneration has not been accomplished. Tissue engineering using a combination of cells and scaffolds is considered to be a viable alternative strategy. We have shown that autologous transplantation of periodontal ligament-derived multipotent mesenchymal stromal cell (PDL-MSC) sheets regenerates periodontal tissue in canine models. However, the indications for autologous cell transplantation in clinical situations are limited. Therefore, this study evaluated the safety and efficacy of allogeneic transplantation of PDL-MSC sheets using a canine horizontal periodontal defect model. Canine PDL-MSCs were labeled with enhanced green fluorescent protein (EGFP) and were cultured on temperature-responsive dishes. Three-layered cell sheets were transplanted around denuded root surfaces either autologously or allogeneically. A mixture of β-tricalcium phosphate and collagen gel was placed on the bone defects. Eight weeks after transplantation, dogs were euthanized and subjected to microcomputed tomography and histological analyses. RNA and DNA were extracted from the paraffin sections to verify the presence of EGFP at the transplantation site. Inflammatory markers from peripheral blood sera were quantified using an enzyme-linked immunosorbent assay. Periodontal regeneration was observed in both the autologous and the allogeneic transplantation groups. The allogeneic transplantation group showed particularly significant regeneration of newly formed cementum, which is critical for the periodontal regeneration. Serum levels of inflammatory markers from peripheral blood sera showed little difference between the autologous and allogeneic groups. EGFP amplicons were detectable in the paraffin sections of the allogeneic group. These results suggest that allogeneic PDL-MSC sheets promoted periodontal tissue regeneration without side effects. Therefore, allogeneic transplantation of PDL-MSC sheets has a potential to become an alternative strategy for periodontal regeneration.
Research on Two Novel Channel Estimation Algorithms for Block Transmission Systems with Two Transmit Antennas Over Frequency-Selective Fading Channels

This paper presents two novel channel estimation algorithms for block transmission systems with two transmit antennas over frequency-selective fading channels: time-domain-processing-based algorithm (TDPBA) and frequency-domain-processing-based algorithm (FDPBA). Performance analysis of the two novel algorithms and simulation results show that both can achieve good channel estimation performance at low and middle Signal-to-Noise Power Ratios (SNRs), but TDPBA's computational complexity is lower than that of FDPBA, while the "flat-roof effect" which appears in TDPBA at high SNRs can be avoided in FDPBA.
Integrating Correlations

Introduction In the last few years the quantitative modelling of credit risk has received a lot of attention within the financial industry. Several models have been released to the public, notably CreditRisk+, CreditMetrics, Credit Portfolio View [CR97, CM97, CP98]. Although different approaches to credit risk are used, studies have shown that the models yield similar results if the parameters are set in a consistent way, see for example [KO98]. CreditRisk+ in particular is ideal for practical implementation and has some nice features, namely: (a) few assumptions have to be made, (b) the methodology is very transparent and based on concepts already used in the insurance business and (c) the loss distribution can be calculated analytically in an efficient way using an iterative procedure.
Effectiveness of Inspiratory Termination Synchrony with Automatic Cycling During Noninvasive Pressure Support Ventilation

Background Pressure support ventilation (PSV) is a standard method for non-invasive home ventilation. A bench study was designed to compare the effectiveness of patient-ventilator inspiratory termination synchronization with automated and conventional triggering in various respiratory mechanics models. Material/Methods Two ventilators, the Respironics V60 and Curative Flexo ST 30, connected to a Hans Rudolph Series 1101 lung simulator, were evaluated using settings that simulate lung mechanics in patients with chronic obstructive pulmonary disease (COPD), acute respiratory distress syndrome (ARDS), or normal lungs. Ventilators were operated with automated (Auto-Trak) or conventional high-, moderate-, and low-sensitivity flow-cycling software algorithms, 5 cmH2O or 15 cmH2O pressure support, 5 cmH2O positive end-expiratory pressure (PEEP), and an air leak of 25–28 L/min. Results Both ventilators adapted to the system leak without requiring adjustment of triggering settings. In all simulated lung conditions, automated cycling resulted in shorter triggering delay times (<100 ms) and lower triggering pressure-time product (PTPt) values. Tidal volumes (VT) increased with lower conventional cycling sensitivity level. In the COPD model, automated cycling had higher leak volumes and shorter cycling delay times than in conventional cycling. Asynchronous events were rare. Inspiratory time (Tinsp), peak expiratory flow (PEF), and cycling off delay time (Cdelay) increased as a result of reduction in conventional cycling sensitivity level. In the ARDS and normal adult lung models, premature cycling was frequent at the high-sensitive cycling level. Conclusions Overall, the Auto-Trak protocol showed better patient-machine cycling synchronization than conventional triggering. This was evident by shorter triggering time delays and lower PTPt.
Interacting galaxies and cosmological parameters

We propose a (physical)-geometrical method to measure Ωm◦ and ΩΛ◦, the present rates of the density cosmological parameters for a Friedmann-Lemaitre universe. The distribution of linear separations between two interacting galaxies, when both of them undergo a first massive starburst, is used as a standard of length. Statistical properties of the linear separations of such pairs of “interactivated” galaxies are estimated from the data in the Two Degree Field Galaxy Redshift Survey. Synthetic samples of interactivated pairs are generated with random orientations and a likely distribution of redshifts. The resolution of the inverse problem provides the probability densities of the retrieved cosmological parameters. The accuracies that can be achieved by that method on Ωm◦ and ΩΛ◦ are computed depending on the size of ongoing real samples. Observational prospects are investigated as the foreseeable surface densities on the sky and magnitudes of those objects.
Live Logic TM : Method for Approximate Knowledge Discovery and Decision Making

Live Logic is an integrated approach for support of the learning and decision making in conditions of uncertainty. The approach covers both induction of probabilistic logical hypotheses from known examples and deduction of the plausible solution for an unknown case based on the inducted hypotheses. The induction method generalizes empirical data, discovering statistical patterns, expressed in logical language. The deduction method uses multidimensional ranking to reconcile contradictory patterns exhibited by a particular case. The method was applied on clinical data of the patients with prostate cancer who underwent prostatectomy. The goal was to predict biochemical failure based on the preand postoperative status of the patient. The patterns found by the method proved to be insightful from the pathologist’s point of view. Most of them had been confirmed on the control dataset. In our experiments, the predictive accuracy of the Live Logic was also higher than that of other tested methods.
Data + Intuition: A Hybrid Approach to Developing Product North Star Metrics

"You make what you measure" is a familiar mantra at data-driven companies. Accordingly, companies must be careful to choose North Star metrics that create a better product. Metrics fall into two general categories: direct count metrics such as total revenue and monthly active users, and nuanced quality metrics regarding value or other aspects of the user experience. Count metrics, when used exclusively as the North Star, might inform product decisions that harm user experience. Therefore, quality metrics play an important role in product development. We present a five-step framework for developing quality metrics using a combination of machine learning and product intuition. Machine learning ensures that the metric accurately captures user experience. Product intuition makes the metric interpretable and actionable. Through a case study of the Endorsements product at LinkedIn, we illustrate the danger of optimizing exclusively for count metrics, and showcase the successful application of our framework toward developing a quality metric. We show how the new quality metric has driven significant improvements toward creating a valuable, user-first product.
Automated simulation of flapper and foil system

Numerical approach using the large-eddy simulation based on the compressible hydrodynamic equations is developed and employed in simulating on turbulent flow around a system of two flappers and a stationary foil. Similar conclusions concerning the mean flow profiles and turbulent intensity can be found in the simulation of flow around the flapper and foil system. Extensive studies are conducted on the effect of the unsteady outer flow, which is produced by the two upstream flappers, on the boundary layer of the stationary foil. As the unsteadiness in the outer flow is small, the response of the boundary of the stationary foil is mainly the first harmonic of the flappers' oscillation. An interesting phenomenon of the tangential velocity phase shifting in the stationary foil's boundary layer is observed.
ANALYSIS OF A QUADRATIC SYSTEM OBTAINED FROM A SCALAR THIRD ORDER DIFFERENTIAL EQUATION

In this article, we study the nonlinear dynamics of a quadratic system in the three dimensional space which can be obtained from a scalar third order differential equation. More precisely, we study the stability and bifurcations which occur in a parameter dependent quadratic system in the three dimensional space. We present an analytical study of codimension one, two and three Hopf bifurcations, generic Bogdanov-Takens and fold-Hopf bifurcations.
Three-dimensional factorization of size-resolved organic aerosol mass spectra from Mexico City

Abstract. A size-resolved submicron organic aerosol composition dataset from a high-resolution time-of-flight mass spectrometer (HR-ToF-AMS) collected in Mexico City during the MILAGRO campaign in March 2006 is analyzed using 3-dimensional (3-D) factorization models. A method for estimating the precision of the size-resolved composition data for use with the factorization models is presented here for the first time. Two 3-D models are applied to the dataset. One model is a 3-vector decomposition (PARAFAC model), which assumes that each chemical component has a constant size distribution over all time steps. The second model is a vector-matrix decomposition (Tucker 1 model) that allows a chemical component to have a size distribution that varies in time. To our knowledge, this is the first report of an application of 3-D factorization models to data from fast aerosol instrumentation, and the first application of this vector-matrix model to any ambient aerosol dataset. A larger number of degrees of freedom in the vector-matrix model enable fitting real variations in factor size distributions, but also make the model susceptible to fitting noise in the dataset, giving some unphysical results. For this dataset and model, more physically meaningful results were obtained by partially constraining the factor mass spectra using a priori information and a new regularization method. We find four factors with each model: hydrocarbon-like organic aerosol (HOA), biomass-burning organic aerosol (BBOA), oxidized organic aerosol (OOA), and a locally occurring organic aerosol (LOA). These four factors have previously been reported from 2-dimensional factor analysis of the high-resolution mass spectral dataset from this study. The size distributions of these four factors are consistent with previous reports for these particle types. Both 3-D models produce useful results, but the vector-matrix model captures real variability in the size distributions that cannot be captured by the 3-vector model. A tracer m/z-based method provides a useful approximation for the component size distributions in this study. Variation in the size distributions is demonstrated in a case study day with a large secondary aerosol formation event, in which there is evidence for the coating of HOA-containing particles with secondary species, shifting the HOA size distribution to larger particle sizes. These 3-D factorizations could be used to extract size-resolved aerosol composition data for correlation with aerosol hygroscopicity, cloud condensation nuclei (CCN), and other aerosol impacts. Furthermore, other fast and chemically complex 3-D datasets, including those from thermal desorption or chromatographic separation, could be analyzed with these 3-D factorization models. Applications of these models to new datasets requires careful construction of error estimates and appropriate choice of models that match the underlying structure of those data. Factorization studies with these 3-D datasets have the potential to provide further insights into organic aerosol sources and processing.
Recommendations in social media for brand monitoring

We present a recommendation system for social media that draws upon monitoring and prediction methods. We use historical posts on some focal topic or historical links to a focal blog channel to recommend a set of authors to follow. Such a system would be useful for brand managers interested in monitoring conversations about their products. Our recommendations are based on a prediction system that trains a ranking Support Vector Machine (RSVM) using multiple features including the content of a post, similarity between posts, links between posts and/or blog channels, and links to external websites. We solve two problems, Future Author Prediction (FAP) and Future Link Prediction (FLP), and apply the prediction outcome to make recommendations. Using an extensive experimental evaluation on a blog dataset, we demonstrate the quality and value of our recommendations.
Statistical Based Waveform Classification for Cloud Intrusion Detection

In recent years, many approaches have been proposed for intrusion detection. In this paper, we propose a cloud intrusion detection with a new statistical waveform based classification. It records network connections over a period of time to form a waveform, and then computes the suspicious characteristics of the waveform. It classifies the intrusion with these selected waveform features. In our evaluation, a DARPA Intrusion Detection Data Sets has been used in our evaluation, and the preliminary results confirmed that our approach is feasible.
Initializing reservoirs with exhibitory and inhibitory signals using unsupervised learning techniques

The trend of Reservoir Computing (RC) has been gaining prominence in the Neural Computation community since the 2000s. In a RC model there are at least two well-differentiated structures. One is a recurrent part called reservoir, which expands the input data and historical information into a high-dimensional space. This projection is carried out in order to enhance the linear separability of the input data. Another part is a memory-less structure designed to be robust and fast in the learning process. RC models are an alternative of Turing Machines and Recurrent Neural Networks to model cognitive processing in the neural system. Additionally, they are interesting Machine Learning tools to Time Series Modeling and Forecasting. Recently a new RC model was introduced under the name of Echo State Queueing Networks (ESQN). In this model the reservoir is a dynamical system which arises from the Queueing Theory. The initialization of the reservoir parameters may influence the model performance. Recently, some unsupervised techniques were used to improve the performance of one specific RC method. In this paper, we apply these techniques to set the reservoir parameters of the ESQN model. In particular, we study the ESQN model initialization using Self-Organizing Maps. Additionally, we test the model performance initializing the reservoir employing Hebbian rules. We present an empirical comparison of these reservoir initializations using a range of time series benchmarks.
Multi-view Sequence-data Representation and Non-metric Distance-function Learning

A constant false alarm rate (CFAR) apparatus is responsive to detected transitions in the clutter signal level to remove certain range cells from the alarm threshold calculation process, whereby the CFAR detector retains maximum sensitivity in the vicinity of clutter transitions while avoiding increased false alarm rates. A transition detection apparatus searches for transitions in range cells preceding the threshold calculation window and controls switches which remove range cells from the threshold calculation as the transition is shifted into and through the threshold calculation window.