Miracle-dz/UniFAR-FTUs huggingface dataset:
A SAS-Tool for Managing Secondary Cell Suppression on Sets of Linked Tables by τ-Argus Modular

The modular optimisation algorithm offered by the software package τ-Argus is an efficient algorithm for secondary cell suppression. Secondary cell suppression in a linked tables setting is still, however, an elaborate and complex task even if the τ-Argus modular tool is used. Although a version of τ-Argus that deals with linked tables structures is soon to appear, it has to be expected that early versions will still be limited regarding the complexity of settings that it can handle. For that reason a SAS-tool has been developed at the Federal Statistical Office of Germany which reduces the effort and complexity in a linked tables application to a great extent. This paper presents the SAStool and outlines its conceptual design. A special feature of the tool will be demonstrated at the example of a public demo data set representing typical structures of business data applications. This feature enables to properly suppress a table displaying the sums of cells of a set of tables which have been released already which is the typical situation in the EU where the Eurostat publications consist of aggregated member state level tables.
Capacity withholding equilibrium in wholesale electricity markets

This paper is a quantitative study of the capacity withholding incentives in the deregulated wholesale electricity markets and resulting price spikes. For the analysis we used an N-player Nash equilibrium model based on marginal cost functions of the generating firms assuming completely inelastic industry demand and complete information. The current results show that in the case of continuous marginal costs the withholding incentive always exists and the total withholding increases with the increase of the curvature of marginal cost functions and the extent of heterogeneity of the generating firms. The discrete form of real marginal cost functions imposes certain restrictions on the withholding. The analysis shows that there exists a threshold level of market demand below which no withholding occurs and above which the withholding becomes beneficial. The curvature and heterogeneity of marginal cost functions also affect the level of this threshold. The model is applied to the power generation in California ISO area.
Drought Severity Assessment in Arid Area of Thal Doab using Remote Sensing and GIS

Thal Doab, which is situated between rivers Indus, Jhelum and Chenab, bears extremely dry weather conditions with very low rainfall. Gram is the most important Rabi crop sown in this region. During drought period, the production of this crop is affected badly. Globally there are four known categories of drought i.e. Meteorological, Agricultural, Hydrological and Socio-economic. In this study meteorological and agricultural parameters were analyzed for the variety of data for the years i.e. 1989, 1995, 2001 and 2007. Initially anomalies were calculated for NDVI, rainfall and crop yield to evaluate percent change in different years. Crop yield and NDVI possess negative anomalies during drought years while in case of non-drought years, static and positive anomalies were found. Then Standardized Precipitation Index was computed on six monthly bases for the time series of nineteen years (1989-2007). The correlation regression analysis was preformed to identify the dependency level among different parameters. Positive correlation was found among SPI, NDVI, crop yield, rainfall anomalies, NDVI anomalies and total seasonal rainfall. Similarly ET was also calculated from the data. o Negative correlation between crop yield and ET was observed. The weighted overlay analysis method was also o applied by assigning relative ranks to each factor. The meteorological and agricultural drought were assessed by overlaying SPI, rainfall anomalies, ET , crop yield, NDVI anomalies and water table depth data. The well o known 2001 drought year was confined by using above method applied in this research. Finally drought severity maps were generated by integrating the meteorological and agricultural drought severity maps, indicating the areas facing combine hazard condition. The maps can be extremely helpful to the planners to reveal the situation in the area.
UNITED DIRECTED COMPLEX DYNAMICAL NETWORKS WITH MULTI-LINKS : NOVEL SYNCHRONIZATION STABILITY CRITERIA

Networks with multi-links can be found everywhere in our daily life such as communication networks, transport networks and social networks. In view of a method of network split according to the different nature of time-delay, the model of the united directed complex dynamical networks with multi-links is considered in the paper. Then the problem of synchronization stability analysis for the united directed complex dynamical network is further discussed. Taking advantage of linear matrix inequalities (LMI), some novel Lyapunov functions are constructed. And then some new general stability criteria are proposed of synchronization state in the united directed complex dynamical networks by use of the Lyapunov stability theory. Finally, numerical simulations are provided to show the effectiveness and feasibility of the proposed theorems.
Explicit Instructional Interactions

A critical aspect of intervention research is investigating the active ingredients that underlie intensive interventions and their theories of change. This study explored the rate of instructional interactions within treatment groups to determine whether they offered explanatory power of an empirically validated Tier 2 kindergarten mathematics intervention. Direct observations were conducted in 46 interventions groups, involving approximately 228 students. Multilevel structural equation models revealed that fall mathematics achievement significantly and negatively predicted the rate of academic feedback. Specifically, intervention groups with lower student mathematics achievement at the start of the kindergarten year received higher rates of academic feedback. Analyses also suggested that latent rates of instructional interactions were not significantly correlated with gains on student mathematics outcomes. Implications are discussed in relation to specifying the underlying mechanisms to intensify mathematics interventions, documenting local adaptation of intervention implementation, and examining possible threshold effects of instructional interactions.
Estimating the reduction of vibrations in MACAO and ALTAIR AO systems using H2 control synthesis

Abstract. Vibrations and external disturbances of various origins (e.g. structural, wind, axis control) have been shown to degrade the image quality at the instrument focus of the telescopes. Furthermore, the next generation of extremely large telescopes (ELTs) and their associated adaptive optics (AO) systems will reach new levels of high angular resolution, thus reducing vibrations represents becomes highly desirable to ensure that the scientific instruments will take full advantage of this unprecedented resolution. We present a method that aims to mitigate the effect of vibrations at Paranal's MACAO and Gemini North’s ALTAIR instruments. A frequency-based controller, based on the H2 synthesis technique [1,2], is used to maximize the performance of the tip and tilt closed-loop systems. The pseudo open-loop slopes are reconstructed from on-sky data and then used to find the controller that minimizes the variance of the tip-tilt residuals in an off-line simulation of the closed-loop system.
Working Paper WP / 14 / 030 Foreign aid , investment and fiscal policy behavior : theory and empirical evidence

The paper provides theoretical and empirical justifications for the instrumentality of foreign aid in stimulating private investment and fixed capital formation through fiscal policy mechanisms. We propose an endogenous growth theory based on an extension of Barro (1990) by postulating that the positive effect of aid mitigates the burden of the taxation system on the private sector of recipient countries. The empirical validity is based on 53 African countries for the period 1996-2010. While the findings on the tax effort channel are overwhelmingly consistent with theory across specifications and fundamental characteristics, those of the government expenditure channel are a little heterogeneous but broadly in line with the theoretical postulations. Justification for the slight heterogeneity and policy implications are discussed. JEL Classification: B20; F35; F50; O10; O55
Relationship between Urinary and Serum Level of Adiponectin with Disease Activity in Patients with Lupus in Comparison with Control Group

Objective: Systemic lupus erythematosus (SLE) is a connective tissue disease with chronic and recurrent and inflammatory progression. Antiinflammatory cytokines such as adiponectin change in this disease like other inflammatory diseases. This study aimed to evaluate the relationship between urinary and serum level of Adiponectin with disease activity in patients with SLE. Methods and materials: In this case-control study, 80 women referred to the rheumatology clinic of 5 Azar Hospital in Gorgan, divided to case and control group. Then urinary and serum level of adiponectin measured by ELISA kit and disease activity evaluated by SLE disease activity index were Blood samples were taken from both groups and serum levels of interleukin -2 measured by AviBion human IL-2. Data analysis conducted by SPSS software (version 16) and by using descriptive statistics and statistical tests. Results: Mean serum level of adiponectin in case group had statistically significant association with control group (p < 0.001) and mean urinary adiponectin level in case group had statistically significant correlation with control group too (p < 0.035). Only serum level of adiponectin was significantly associated with SLE disease activity index in case group (r = 0.63, p < 0.0001) and urinary level of adiponectin was significantly associated with renal involvement (r = 0.59, p < 0/0001). Conclusions: Findings of the present study showed the relationship between serum level of Adiponectin with disease activity in patients with SLE. So this biomarker can be used for effective treatment and decrease of complications. From: 1, Bone, Joint and Connective tissue disorder Research Center, sayyad shirazi hospital ,Gorgan, Iran, Department of Statistics, Medical School, Golestan University of Medical Sciences ,Gorgan, Iran and 4, Clinical Student Research Committee, Faculty of Medicine, sayyad shirazi hospital ,Golestan University of Medical Sciences, Gorgan, Iran. Correspondence: Sima Sedighy and maryam faghani, Golestan University of Medical Sciences, sayyad shirazi hospital, Gorgan, Iran. E-mail:simasedighy@yahoo.com or Sima_sedigh@yahoo.com Relationship between Urinary and Serum Level of Adiponectin with Disease Activity in Patients with Lupus
International Conference on Space Optics — ICSO 2018 Chania , Greece 9 – 12 October 2018

The Ultra Fine Sun Sensor (UFSS) on board the HINODE solar observing satellite is one of the most successful sun sensors. It is the linear CCD sun sensor with a special detection method using multiple slits, called the periodic reticle. The angular resolution of 0.14 arcsec in the noise equivalent angle (NEA) and 1 arcsec stability were achieved by the sensor head, of 1.2 kg weight. The concept of the detection method and processing algorithm of the Sun’s direction is described. The system is modeled and the dynamic response of the system is characterized by the first-order lag system. By utilizing this characteristic, a resolution improvement three times higher can be expected by adjusting the parameters with a small modification to the HINODE UFSS processing algorithm. The design for a new UFSS for the next generation solar observation satellite, SOLAR-C, shall include these modifications. The thermomechanical design is also reviewed to improve stability and a design policy is obtained.
4 A . 2 CONVECTIVE HAZARDS ASSESSMENT PROCESS : REVISED RICKS INDEX

A technique initially developed in 1986 to estimate convective threats based on observed and forecast upper air soundings has been refined and tested in an operational setting since 1992. This technique scales expected convective weather outcomes based on the pressure difference between the freezing level and the midpoint of a parcel’s Convectively Available Positive Energy. The output has been empirically scaled from 0 to 216, with values exceeding 150 indicating severe thunderstorms are likely. Routine application of this method has shown it to be successful, primarily by increasing situational awareness of potential hazardous weather situations. Computer software has been developed to facilitate the computation of the likelihood of other severe weather parameters, including convective wind gusts, heavy rainfall potential, expected hail size, and tornado intensity. Since 2004, software has been configured to analyze forecast soundings from the National Center for Environmental Prediction’s (NCEP’s) North American Mesoscale Model. The output is a variety of weather parameters, similar to the operational model output statistics, but also includes the convective hazards parameters. The software has been recently modified to also analyze forecast soundings from the NCEP’s Global Forecast System. Evaluation of the performance of the process has revealed some benefits, but also some obstacles in automating the computations. The computation and interpretation of the Ricks Index will be demonstrated, and some applications and verification data will be shown. Examples of the output statistics, operational strategies and plans * Corresponding author address: Robert J. Ricks, Jr., NOAA/NWS Forecast Office, New Orleans/Baton Rouge, LA, Slidell, LA 70420-5423; e-mail: robert.ricks@noaa.gov. for future development will also be presented to demonstrate a total convective hazards assessment process.
Social Competence in Urban Alternative Schools SOCIAL COMPETENCE IN URBAN ALTERNATIVE SCHOOLS

This exploratory grounded theory study examined teacher perspectives of social competence in urban alternative high schools. Research questions elicited teachers’ conceptualizations of social competence and descriptions of how their views on students’ social-emotional behaviors influence pedagogy. The specific school context was behavior-focused alternative schools, which serve a disproportionate number of Black students and students with disabilities. Findings indicated urban alternative high school teachers highly value and prioritize within their classrooms the learning and application of specific social competencies perceived to influence post-high school employability. Implications are discussed.
Real Time Estimation Of Modal Parameters Of Non-Stationary ... 3 REAL TIME ESTIMATION OF MODAL PARAMETERS OF NON-STATIONARY SYSTEMS USING ADAPTIVE WAVELET FILTERING AND RECURSIVE LEAST SQUARE ALGORITHM

The paper presents a method of modal parameter estimation based on RLS (Recursive Least Square) algorithm, and wavelet filtering. The wavelet filtering gives possibility to decoupling frequency components of signal response of structure. This operation can also reduce the order of the signal model estimated by RLS algorithm. An additional advantage of this method is the possibility of adapting the wavelet filter parameters to the changing parameters of the system. Reduced model order significantly reduces the time of estimation of modal parameters, which enables the real – time implementation of the method. Due to recursively updated covariance matrix of model parameters, the confidence intervals of modal parameters can be also estimated. All routines have been implemented and tested in MATLAB®. The method have been tested on simulated data delivered by an AIRBUS team and on the test bed with a variable stiffness.
1 Problem Formulation and Overview of the Proposed Framework

Multimodal sentiment analysis has recently received an increasing interest. However, most methods have considered that text and image modalities are always available at test time. This assumption is often violated in real environments (e.g. social media) since users do not always publish a text with an image. In this paper we propose a method based on a multitask framework to combine multimodal information when it is available, while being able to handle the cases where a modality is missing. Our model contains one classifier for analyzing the text, another for analyzing the image, and another performing the prediction by fusing both modalities. In addition to offer a solution to the problem of a missing modality, our experiments show that this multitask framework improves generalization by acting as a regularization mechanism. We also demonstrate that the model can handle a missing modality at training time, thus being able to be trained with image-only and text-only examples.
J ul 2 00 5 Random multi-index matching problems

The multi-index matching problem (MIMP) generalizes the well known matching problem by going from pairs to d-uplets. We use the cavity method from statistical physics to analyze its properties when the costs of the d-uplets are random. At low temperatures we find for d ≥ 3 a frozen glassy phase with vanishing entropy. We also investigate some properties of small samples by enumerating the lowest cost matchings to compare with our theoretical predictions.
Efficient Soil Moisture Measurement Using Microwave Surface Reflection Method

Soil moisture determination is critical as the parameter affects the physical and characteristics of soil in civil, geological and agricultural industry. However, effective nondestructive model for accurate soil moisture determination is challenging to be discovered. In this article, the concept and development of soil moisture determination via ground penetrating radar (GPR) principle is explained. The system is designed to be used with standard horn antenna with a sweep frequency of 1.7 – 2.6GHz along with vector network analyzer (VNA). The proposed system can determine soil moisture of three types of soil samples such as sand, loamy, and clay with high degree of accuracy. In this work, microwave surface reflection method is applied to analyze the effect of soil moisture with its electrical properties using GPR principle. The result of the research is promising with high percentage of agreement with Topp theoretical value. The highest values are 61% 42% 54% for sand, clay and loamy, respectively. For system validation, a new soil type is used for measurement, and the result has an accuracy of 93%. By using the proposed developed models, soil moisture estimation can be determined with minimal data input through a novelty GPR principle. Index Term-Ground-penetrating-radar, microwave, radio wave, soil moisture, surface reflection
Operator Vectorization Library – A TensorFlow Plugin

TensorFlow is an interface for implementing machine learning applications that can be accelerated by using Graphics Processing Units (GPUs). It is rapidly becoming a standard tool in this space. TensorFlow consists of a high level API for constructing stateful dataflow graphs and a runtime which distributes and schedules the evaluation of operations in the graph onto various compute devices. TensorFlow provides an extensive library of operators, particularly those commonly used for machine learning applications. However for some deep learning applications such as recurrent networks, graph analytics or problems solved by dynamic programming, the library operators are not enough. The Operator Vectorization Library, or OVL, is a Python plugin into TensorFlow. OVL provides a Python API for defining high performance custom operators for the TensorFlow framework. It enables TensorFlow users to easily write, test, and use custom operators within Python and the OVL API instead of programming in C++/CUDA, without sacrificing performance. Applications running on GPUs may compute operations faster than they transfer data in and out of the host memory. Thus, these applications are memory bandwidth limited. Kernel fusion is the process of taking multiple operators, represented as vertices in the data flow graph, and merging them into a single operator. Fusion improves performance because it eliminates the fixed costs of launching an operator and can lower bandwidth requirements by eliminating extraneous copies from the compute device out to main memory and back, which may happen between operator calls. Despite the simplicity of programming and the acceleration gains exhibited by TensorFlow for some applications, the architecture of the TensorFlow framework does not support kernel fusion. The OVL optimizer provides automated kernel fusion of OVL operators at runtime. We chose a recurrent neural network known as the long short-term memory (LSTM) as a test case for the OVL optimizer. Using OVL and its optimizer we show a 2.3x speed-up over a pure TensorFlow implementation. OVL was released under the Apache 2.0 license in August 2016 at https://github.com/opveclib/opveclib. This paper describes the OVL interface and implementation. External Posting Date: November 3, 2016 [Fulltext] Internal Posting Date: November 3, 2016 [Fulltext]  Copyright 2016 Hewlett Packard Enterprise Development LP Operator Vectorization Library – A TensorFlow Plugin
Client Solvency Estimation Using Intellectual Data Analysis Approach

This article is devoted to the estimation s solvency and is an important problem for the banking system and other financial companies that provide loans to their clients. The questions devoted to the main methods of data mining for the collection of statistical information on clients, aimed at assessing the probability of default on loan repayment. To solve the problem, modern estimation systems based on mathematical models have been developed that provide the possibility of analyzing and assessing the client's status regarding their solvency. Conducted assess the creditworthiness using approaches such as decision trees, cluster analysis (k-means), hierarchical clustering, nearest neighbor method, Binary choice regression models, nonlinear regression logit models, probit model, Bayesian networks.
REDUCTION OF NOx IN A DIESEL ENGINE USING SPLIT INJECTION APPROACH

One of the important goals in diesel engine research is the development of means to reduce the emissions of oxides of nitrogen (NOx) and soot particulates, and in this endeavour advanced techniques like split injection/multiple injection, exhaust gas recirculation (EGR) etc. are being developed. The principle of split injection is that when fuel is injected in two pulses it engenders a reduction in the peak combustion chamber temperature which ensures the reduction in NOx emission. In this study to achieve split injection in an economical manner, a double lobed cam has been designed incorporating the optimum split and dwell. Experimental study indicates that 50-50 split with 10 degree crank angle dwell is the optimum to achieve a viable reduction in NOx without any major penalty in soot and power. A 15% reduction in NOx and 10% reduction in soot was observed on incorporating these results.
Deposited in DRO : 29 March 2018 Version of attached le :

With the development of Smart Grid, there is an increasing need for the inter discipline research, analysis and evaluation, especially in the joint research area of communication system and power system. In this paper, we propose a Cognitive Radio enabled Smart Grid testbed, which is able to provide real time emulation of the real Smart Grid systems. A prototype with USRP N210, data acquisition and actuator module and Real Time Digital Simulator is implemented, which verifies the framework of the proposed testbed architecture. Evaluation cases show that the proposed testbed is able to provide an average of 9.7ms round trip communication latency and validate real time Smart Grid applications such as voltage stability control.