Miracle-dz/UniFAR-FTUs huggingface dataset:
THE ECONOMIC CYCLE AND THE INNOVATION ACTIVITY OF THE POLISH INDUSTRY SYSTEM

Industrial structures in the “catching up” type of countries usually are not too competitive in nature, and mainly they differ in the low share of high-technology products in the international trade. The aim of the conducted studies was an attempt to search for directions, as well as the power of impact of different phases of the business cycle on the innovation activity of the Polish industrial system. Consequently, this was to allow to determine the boundary conditions for the national network of innovations and its model structure, which would take into account the specificity of Poland. The methodical part of the paper was based on the theory of probability (probit modelling). Based on the analysis performed based on 5209 industrial companies (questionnaire survey) it was stated that in the prosperity phase the implementation of the innovation activity is significantly higher than in other phases of the business cycle. On the other hand, during recession and stagnation, the innovation activity is a less common phenomenon, but is not completely abandoned. Research results did not confirm the occurrence of the counter-cyclical approach to the conducted innovation activity in the national industrial system. The economic situation is thus an important factor, which influences the decision whether to undertake, activate or, in some cases, limit the innovation activity in companies. Therefore, there is a need to take into account the existing market conditions in the programming of the innovation policy within the impact on the phenomenon of the innovation processes in Poland.
The Combined Effect of Applying Feature Selection and Parameter Optimization on Machine Learning Techniques for Solar Power Prediction

This paper empirically shows that the combined effect of applying the selected feature subsets and optimized parameters on machine learning techniques significantly improves the accuracy for solar power prediction. To provide evidence, experiments are carried on in two phases. For all the experiments the machine learning techniques namely Least Median Square (LMS), Multilayer Perceptron (MLP) and Support Vector Machine (SVM) are used. In the first phase five well-known wrapper feature selection methods are used to obtain the prediction accuracy of machine learning techniques with selected feature subsets and default parameter settings. The experiments from the first phase demonstrate that holding the default parameters, LMS, MLP and SVM provides better prediction accuracy (i.e. reduced MAE and MASE) with selected feature subsets rather than without selected feature subsets. After getting improved prediction accuracy from the first phase, the second phase continues the experiments to optimize machine learning parameters and the prediction accuracy of those machine learning techniques are re-evaluated through adopting both the optimized parameter settings and selected feature subsets. The comparison between the results of two phases clearly shows that the later phase (i.e. machine learning techniques with selected feature subsets and optimized parameters) provides substantial improvement in the accuracy for solar power prediction than the earlier phase (i.e. machine learning techniques with selected feature subsets and default parameters). Experiments are carried out using reliable and real life historical meteorological data. The machine learning accuracy of solar radiation prediction is justified in terms of statistical error measurement and validation metrics. Experimental results of this paper facilitate to make a concrete verdict that providing more attention and effort towards the feature subset selection and machine learning parameter optimization (e.g. combined effect of selected feature subsets and optimized parameters on prediction accuracy which is investigated in this paper) can significantly contribute to improve the accuracy of solar power prediction.
Institutions, Ratings and Economic Growth: In Search of Reliable Indicators

The ratings of political institutions are well-known and widely used in academic literature. These ratings are mostly based on expert evaluations. However, such evaluations can be subjective and occasionally driven by ideological considerations. In this paper we propose two new indicators of institutional quality for 154 countries. These indicators are constructed in a way that minimizes the subjectivity of the evaluations. Only the presence or absence of a particular institutional phenomenon is identified. This puts much less weight on possible bias and makes easy to verify. We show that these indices predict economic growth better than those commonly used, primarily because they include information about institutions that has been accumulated over a period of approximately two centuries
Value Capture for Transportation Finance: Technical Research Report

As vehicles become more fuel-efficient and overall levels of travel stagnate in response to increases in fuel prices, conventional sources of revenue for transportation finance such as taxes on motor fuels have been put under increasing pressure. One potential alternative as a source of revenue is a set of policies collectively referred to as value capture policies. In contrast to fuel taxes and other instruments that impose charges on users of transportation networks, value capture policies seek to generate revenue by extracting a portion of the gains in the value of land that result from improvements to transportation networks. In this report the authors identify a set of eight policies that contain elements of the value capture approach. These policies include land value taxes, tax increment financing, special assessments, transportation utility fees, development impact fees, negotiated exactions, joint development, and air rights. Each of the policies is evaluated according to four criteria: 1) efficiency, which relates to how well the policies allocate scarce resources, 2) equity, which describes the fairness of resource allocation among different strata of society, 3) sustainability, which refers to the ability of the policy to serve as an adequate, reliable source of transportation revenue, and 4) feasibility, which refers to the degree of political and administrative difficulty associated with each policy. Since these policies are targeted toward use at the state and local level in Minnesota, the authors conclude by examining some legal and administrative issues related to the implementation of each policy with special reference to Minnesota.
A micromachined thin-film gas flow sensor for microchemical reactors

As microchemical systems (MCS) have gained in importance since their introduction in the last decade, it has become recognized that appropriate sensing and control capabilities are needed if MCS are to reach their potential. In this context, we present a study of the working behavior of a novel thin-film micro flow sensor which is integrated with a silicon microreactor with a submillimeter channel. A simple-to-fabricate device based on the concept of calorimetric sensing was chosen as a model structure to understand the important factors controlling sensor performance. Various design options for the sensor were explored by the use of computational fluid dynamics simulations. We found that sensitivity depends strongly on certain design factors. In summary, sensitivity is improved with (a) higher values of the resistors that detect flow-induced temperature changes, (b) shorter distances between the resistor that provides a source of heat and the thermally sensitive resistors and (c) higher input power to the heating resistor. Item (a) was found to have by far the strongest effect of the three. Reproducibility tests were conducted and the sensor exhibited consistent performance throughout the entire test range of 0–20 sccm which is an appropriate fit to the flow capacity of the microchannel. Finally, response time was assessed by simulating the transient behavior of the sensor with a thermal capacitance model, which yielded an accurate prediction of the experimental response of the device. The response time is approximately 70 ms at a typical flow rate of 10 sccm. According to the understanding gained from the model, the sensor response time can be improved by reducing the substrate thickness, using a lower density substrate material, and increasing the convective heat transfer coefficient in the channel.
Highly Efficient Distance-Based Anomaly Detection through Univariate with PCA in Wireless Sensor Networks

Unsupervised anomaly detection (UAD) techniques have received increasing attention in wireless sensor networks (WSNs). However, the high dimensional training data often make sensor nodes unable to sustain in computation, and result in quite expensive communication overhead. The feature reduction techniques make great sense through the reduction of the dimensionality when the features are strongly interrelated. Among these UAD techniques, distance-based anomaly detection (DB-AD) is a special one that allows to be described by a probability model. Based on this observation, DB-AD is explored deeply with a feature reduction technique, principal component analysis (PCA). Through examining the proportion of the variance explained by the first principal component (PC), a new feature reduction approach is proposed for DB-AD in WSNs, which enables to reduce the dimensionality to one in any situation. Specifically, the first PC is alone used for representing the original data as long as it retains most of the variance, otherwise, the information loss is geometrically reverted to neutralize the error. By obtaining a tradeoff between the detection error and performance overload, this approach is significant for resource-constrained WSNs, as the computational complexity and communication overhead will be reduced to a fraction of the original magnitude. Finally, this approach is evaluated with a real WSN dataset.
Time Based Context Cluster Analysis for Automatic Blog Generation

This paper describes the algorithms developed to identify the actions of a community of users. Actions are detected through the processing of raw context data acquired from the users’ smartphones or PDAs by our context awareness platform. Data mining techniques, and in particular cluster analysis, allow us to discover high level information, like the actions performed by the subjects during each day. This leads to the detection of a community of users, linked to each other by the places they have visited and by whom they have met. Natural text description, enriched with Microformats semantic markup, is created for each user’s action. Pictures and movies acquired during the day are added to the text, that is automatically published as a blog post. Clustering algorithms explanation is provided, and the main issues of mining context data are addressed. A brief description of the blog generation is presented, too. Moreover, the perception that the users have of their own daily activities is analyzed, based on our experimental results.
Image-rejection CMOS low-noise amplifier design optimization techniques

This paper reviews and analyzes two reported image-rejection (IR) low-noise amplifier (LNA) design techniques based on CMOS technology, i.e., the second-order active notch filer and third-order passive notch filter. The analyses and discussions are based on the quality factor of filters and the ability of the frequency control. As the solution to deal with the suitable on-chip filter, this paper proposes a new notch-filter topology that can overcome the limitations of the two previous reported studies. In addition, the LNA design method satisfying the power-cons-trained simultaneous noise and input matching, as well as the linearity optimization conditions is introduced. By using the proposed notch filter and proposed design methodology, an IR LNA used in the superheterodyne architecture is implemented. The proposed IR LNA, designed based on 0.18-mum CMOS technology with total current dissipation of 4 mA under 3-V supply voltage, is optimized for a 5.25-GHz wireless local area network with IF frequency of 500-MHz applications. The measurement results show 20.5-dB power gain, lower than 1.5-dB noise figure, -5-dBm input-referred third-order intercept point and an IR of 26 dB
A Primal-Dual Approximation Algorithm for Min-Sum Single-Machine Scheduling Problems

We consider the following single-machine scheduling problem, which is often denoted $1||\sum f_{j}$: we are given $n$ jobs to be scheduled on a single machine, where each job $j$ has an integral processing time $p_j$, and there is a nondecreasing, nonnegative cost function $f_j(C_{j})$ that specifies the cost of finishing $j$ at time $C_{j}$; the objective is to minimize $\sum_{j=1}^n f_j(C_j)$. Bansal and Pruhs recently gave the first constant approximation algorithm with a performance guarantee of 16. We improve on this result by giving a primal-dual pseudo-polynomial-time algorithm based on the recently introduced knapsack-cover inequalities. The algorithm finds a schedule of cost at most four times the constructed dual solution. Although we show that this bound is tight for our algorithm, we leave open the question of whether the integrality gap of the linear program is less than 4. Finally, we show how the technique can be adapted to yield, for any $\epsilon >0$, a polynomial time $(4+\epsilon )$-app.
HIV/AIDS: nutritional implications and impact on human development

HIV/AIDS is associated with biological and social factors that affect the individual's ability to consume and utilize food and to acquire food. These biological and social factors lead to poor nutritional status and weight loss, which are an important cause of morbidity in individuals infected with HIV, resulting in a poor quality of life; weight loss is an important predictor of death from AIDS. The links between nutrition and HIV/AIDS amplify the negative effects of HIV infection on human development at individual, household, community and national levels. For many developing countries the incidence of HIV/AIDS and malnutrition is impeding progress towards achieving the UN millennium development goals. Aggressive interventions to curb the spread of HIV continue to be needed. Concurrent efforts to improve nutrition for populations living with HIV/AIDS should also be given priority.
Characterization of Cardiac-Resident Progenitor Cells Expressing High Aldehyde Dehydrogenase Activity

High aldehyde dehydrogenase (ALDH) activity has been associated with stem and progenitor cells in various tissues. Human cord blood and bone marrow ALDH-bright (ALDHbr) cells have displayed angiogenic activity in preclinical studies and have been shown to be safe in clinical trials in patients with ischemic cardiovascular disease. The presence of ALDHbr cells in the heart has not been evaluated so far. We have characterized ALDHbr cells isolated from mouse hearts. One percent of nonmyocytic cells from neonatal and adult hearts were ALDHbr. ALDHvery-br cells were more frequent in neonatal hearts than adult. ALDHbr cells were more frequent in atria than ventricles. Expression of ALDH1A1 isozyme transcripts was highest in ALDHvery-br cells, intermediate in ALDHbr cells, and lowest in ALDHdim cells. ALDH1A2 expression was highest in ALDHvery-br cells, intermediate in ALDHdim cells, and lowest in ALDHbr cells. ALDH1A3 and ALDH2 expression was detectable in ALDHvery-br and ALDHbr cells, unlike ALDHdim cells, albeit at lower levels compared with ALDH1A1 and ALDH1A2. Freshly isolated ALDHbr cells were enriched for cells expressing stem cell antigen-1, CD34, CD90, CD44, and CD106. ALDHbr cells, unlike ALDHdim cells, could be grown in culture for more than 40 passages. They expressed sarcomeric α-actinin and could be differentiated along multiple mesenchymal lineages. However, the proportion of ALDHbr cells declined with cell passage. In conclusion, the cardiac-derived ALDHbr population is enriched for progenitor cells that exhibit mesenchymal progenitor-like characteristics and can be expanded in culture. The regenerative potential of cardiac-derived ALDHbr cells remains to be evaluated.
Bargaining Regimes and Wage Dispersion

Summary Collective wage contracts impose restrictions on wage-setting. We utilize German linked employer-employee data for blue-collar worker to compute the dispersion of wages and wage components within and across firms under three different wage-setting regimes: Establishments applying industry-wide collective contracts, establishments with firm-level contracts and uncovered establishments. The empirical analysis confirms a lower dispersion of wages and in particular “worker quality indices” for firms applying sectoral collective contracts compared to companies in the other two wage-setting regimes. Between the years 1990, 1995 and 2001 the dispersion of wages increases, and this is valid too for firms covered by industry-wide collective contracts. A sensitivity analysis demonstrates that the results retain their statistical significance and do not change qualitatively if we modify the minimum firm size (establishments with at least 50,100 and 250 employees) or take into account the endogeneity of tenure or self-selection of workers into bargaining regimes.
Semi-supervised hyperspectral classification using active label selection

This paper introduces a new semi-supervised Bayesian approach to hyperspectral image segmentation. The algorithm mainly consists of two steps: (a) semi-supervised learning, by using the LORSAL algorithm to infer the class distributions, followed by (b) segmentation, by inferring the labels from a posterior density built on the learned class distributions and on a Markov random field. Active label selection is performed. Encouraging results are presented on real AVIRIS Indiana Pines data set. Comparisons with state-of-the-art algorithms are also included.
Optimal control in laser-induced population transfer for two- and three-level quantum systems

We apply the techniques of control theory and of sub-Riemannian geometry to laser-induced population transfer in two- and three-level quantum systems. The aim is to induce complete population transfer by one or two laser pulses minimizing the pulse fluences. Sub-Riemannian geometry and singular-Riemannian geometry provide a natural framework for this minimization, where the optimal control is expressed in terms of geodesics. We first show that in two-level systems the well-known technique of “π-pulse transfer” in the rotating wave approximation emerges naturally from this minimization. In three-level systems driven by two resonant fields, we also find the counterpart of the “π-pulse transfer. ” This geometrical picture also allows one to analyze the population transfer by adiabatic passage.
Model Order Reduction via Rational Transfer Function Fitting and Eigenmode Analysis

In this paper, we have proposed a novel model order reduction technique via rational transfer function fitting and eigenmode analysis considering residues. We define a constant as a key in the sorting algorithm as one of correlations in order to sort the order of eigenvalues. It is demonstrated that the accuracy via eigenmode analysis considering residues is improved. The proposed algorithm is a general method to match pole values with frequency domain poles for linear RC and RLC systems. Calculation of pole eigenvalues and eigen vectors can be done with more sophisticated analysis with the same level or smaller cost in the proposed algorithms in comparison to PRIMA. The experimental results show that our algorithm reduces up to 90% errors compared to the existing model order reduction algorithm, such as PRIMA, in wide frequency environment with the same number of poles in comparison.
An Adaptive UWB Waveform with Spectral Sharing Capability

Cognitive radio (CR) is an emerging approach for a more efficient usage of the precious radio spectrum resources, which it considers an expanded view of the wireless channel by managing and adapting various dimensions of time, frequency, space, power, and coding. In this paper, we define the system requirements for cognitive radio, as well as the general architecture and basic physical and link layer functions. In order to self-adapt the UWB pulse shape parameters and maximize system capacity while co-exist with in-band legacy NB systems (WiFi and FWA) in the surrounding environments
Experimental evaluation of multipath TCP schedulers

Today many end hosts are equipped with multiple interfaces. These interfaces can be utilized simultaneously by multipath protocols to pool resources of the links in an efficient way while also providing resilience to eventual link failures. However how to schedule the data segments over multiple links is a challenging problem, and highly influences the performance of multipath protocols. In this paper, we focus on different schedulers for Multipath TCP. We first design and implement a generic modular scheduler framework that enables testing of different schedulers for Multipath TCP. We then use this framework to do an in-depth analysis of different schedulers by running emulated and real-world experiments on a testbed. We consider bulk data transfer as well as application limited traffic and identify metrics to quantify the scheduler's performance. Our results shed light on how scheduling decisions can help to improve multipath transfer.
Interpretation of Drawing : A Review on Line Labeling

Interpreting a drawing is important in computer vision. The designer has to understand the two-dimensional drawing and interpret it in order to reconstruct an engineering object. This paper discuss on drawing interpretation but focusing on line labeling method. Line labeling is a famous method in line drawing interpretation on frontal geometry since decades ago. The line labels indicate which edges bound the visible faces or partial faces of the object and which merely occlude them. Besides working as interpreter, line labeling is useful in identifying impossible drawing and the drawing that not represent solid object.
Defending Government: Why Big Government Works

1. An Introduction to the Issues. 2. How Big Has Government Gotten in the United States? 3. Macro-Deterministic Explanations of Government Growth. 4. Public Choice Explanations of Government Size and Growth. 5. For Whom the Evidence Matters: Conclusions Regarding the Causes of Government Growth. 6. Blaming Government: The Case of Economic Performance. 7. Does an Active Government Necessarily Mean Oppressive Government? 8. Hitler didn't Come to Power via the Health Department. 9. Being Dismal in Policy Analysis or Learning to Love Policymaking. 10. The Freedom to Choose Democratic Politics. References. Index.
RESOLVENT ESTIMATES FOR ABEL INTEGRAL OPERATORS AND THE REGULARIZATION OF ASSOCIATED FIRST KIND INTEGRAL EQUATIONS

In this paper resolvent estimates for Abel integral operators are provided. These estimates are applied to deduce regularizing properties of Lavrentiev’s m -times it- erated method as well as iterative schemes (with the discrepancy principle as corresponding parameter choice or stopping rule, respectively) for solving the corresponding Abel integral equations of the ﬁrst kind.